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Posts Tagged ‘T cells’


Cancer Drugs Shed Light on Rheumatism

Reporter: Irina Robu, PhD

The human body is often described as being ‘at war’. By this, it is meant that the body is constantly under attack from things that are trying to do it harm. These include toxins, bacteria, fungi, parasites and viruses. The human immune system is one of the most effective defense mechanisms known to nature and can sometimes can be overwhelmed by disease. Yet, on occasions our immune systems turn on our own tissue and attack it which can trigger conditions such as type I diabetes, rheumatoid arthritis and lupus.
In the case of rheumatoid arthritis, immune cells start to attack tissues in the joins which causes them to become painful, stiff and swollen. It is known that one third of those who develop rheumatoid arthritis, feel the horrible effects of the disease within two years of its onset.
Immunologist Adrian Hayday, which is a researcher at Francis Crick Institute of London says that the current treatment for rheumatoid arthritis require patients to take the drugs for the rest of their lives. But, researchers such as Hayday found an unexpected ally in the battle against autoimmune disease, cancer.
However, there is a positive consequence to the discovery that cancer immunotherapies have the effect of triggering autoimmune diseases and for the first-time rheumatoid arthritis can be detected at the earliest stages. At present, people are not diagnosed with the condition until symptoms have already made their lives so unpleasant, they have gone to see their doctors. As a result, research backed by Cancer Research UK and Arthritis Research UK, has been launched with the aim of uncovering the roots of autoimmune disease from research on cancer patients.
The scientists mentioned stress that their work is only now start and warn that it will still take several years of research to get substantial results. Nevertheless, uncovering the first stages of an autoimmune disease emerging in a person’s body should give researchers a vital lead in ultimately developing treatments that will prevent or halt a range of conditions that currently cause a great deal of misery and require constant medication.
Our immune defenses consist of a range of cells and proteins that notice invading micro-organisms and attack them. The first line of defense, yet, consists of simple physical barriers similar to skin, which blocks invaders from entering your body. When this defense is penetrated, they are attacked by a number of agents. The key cells, leukocytes seek out and destroy disease-causing organisms. Neutrophils rush to the site of an infection and attack invading bacteria. Helper T-cells give instructions to other cells while killer T-cells punch holes in infected cells so that their contents ooze out. After these macrophages clean up the mess left behind.
Another significant agent is the B-cell, which produces antibodies that lock on to sites on the surface of bacteria or viruses and immobilize them until macrophages consume them. These cells can live a long time and can answer quickly following a second exposure to the same infections. In conclusion, suppressor T-cells act when an infection has been distributed with and the immune system needs to be reassured, the killer cells may keep on attacking, as they do in autoimmune diseases. By slowing down the immune system, regulatory T-cells prevent damage to “good” cells.

Source

https://www.theguardian.com/science/2018/mar/03/immunotherapy-cancer-patients-rheumatoid-arthritis-robin-mckie

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

The CRISPR-Cas9 system has proven to be a powerful tool for genome editing allowing for the precise modification of specific DNA sequences within a cell. Many efforts are currently underway to use the CRISPR-Cas9 system for the therapeutic correction of human genetic diseases. CRISPR/Cas9 has revolutionized our ability to engineer genomes and conduct genome-wide screens in human cells.

 

CRISPR–Cas9 induces a p53-mediated DNA damage response and cell cycle arrest in immortalized human retinal pigment epithelial cells, leading to a selection against cells with a functional p53 pathway. Inhibition of p53 prevents the damage response and increases the rate of homologous recombination from a donor template. These results suggest that p53 inhibition may improve the efficiency of genome editing of untransformed cells and that p53 function should be monitored when developing cell-based therapies utilizing CRISPR–Cas9.

 

Whereas some cell types are amenable to genome engineering, genomes of human pluripotent stem cells (hPSCs) have been difficult to engineer, with reduced efficiencies relative to tumour cell lines or mouse embryonic stem cells. Using hPSC lines with stable integration of Cas9 or transient delivery of Cas9-ribonucleoproteins (RNPs), an average insertion or deletion (indel) efficiency greater than 80% was achieved. This high efficiency of insertion or deletion generation revealed that double-strand breaks (DSBs) induced by Cas9 are toxic and kill most hPSCs.

 

The toxic response to DSBs was P53/TP53-dependent, such that the efficiency of precise genome engineering in hPSCs with a wild-type P53 gene was severely reduced. These results indicate that Cas9 toxicity creates an obstacle to the high-throughput use of CRISPR/Cas9 for genome engineering and screening in hPSCs. As hPSCs can acquire P53 mutations, cell replacement therapies using CRISPR/Cas9-enginereed hPSCs should proceed with caution, and such engineered hPSCs should be monitored for P53 function.

 

CRISPR-based editing of T cells to treat cancer, as scientists at the University of Pennsylvania are studying in a clinical trial, should also not have a p53 problem. Nor should any therapy developed with CRISPR base editing, which does not make the double-stranded breaks that trigger p53. But, there are pre-existing humoral and cell-mediated adaptive immune responses to Cas9 in humans, a factor which must be taken into account as the CRISPR-Cas9 system moves forward into clinical trials.

 

References:

 

https://techonomy.com/2018/06/new-cancer-concerns-shake-crispr-prognosis/

 

https://www.statnews.com/2018/06/11/crispr-hurdle-edited-cells-might-cause-cancer/

 

https://www.biorxiv.org/content/early/2017/07/26/168443

 

https://www.nature.com/articles/s41591-018-0049-z.epdf?referrer_access_token=s92jDP_yPBmDmi-USafzK9RgN0jAjWel9jnR3ZoTv0MRjuB3dEnTctGtoy16n3DDbmISsvbln9SCISHVDd73tdQRNS7LB8qBlX1vpbLE0nK_CwKThDGcf344KR6RAm9k3wZiwyu-Kb1f2Dl7pArs5yYSiSLSdgeH7gst7lOBEh9qIc6kDpsytWLHqX_tyggu&tracking_referrer=www.statnews.com

 

https://www.nature.com/articles/s41591-018-0050-6.epdf?referrer_access_token=2KJ0L-tmvjtQdzqlkVXWVNRgN0jAjWel9jnR3ZoTv0Phq6GCpDlJx7lIwhCzBRjHJv0mv4zO0wzJJCeuxJjzoUWLeemH8T4I3i61ftUBkYkETi6qnweELRYMj4v0kLk7naHF-ujuz4WUf75mXsIRJ3HH0kQGq1TNYg7tk3kamoelcgGp4M7UTiTmG8j0oog_&tracking_referrer=www.statnews.com

 

https://www.biorxiv.org/content/early/2018/01/05/243345

 

https://www.nature.com/articles/nmeth.4293.epdf

 

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Signaling through the T Cell Receptor (TCR) Complex and the Co-stimulatory Receptor CD28

Curator: Larry H. Bernstein, MD, FCAP

 

 

New connections: T cell actin dynamics

Fluorescence microscopy is one of the most important tools in cell biology research because it provides spatial and temporal information to investigate regulatory systems inside cells. This technique can generate data in the form of signal intensities at thousands of positions resolved inside individual live cells. However, given extensive cell-to-cell variation, these data cannot be readily assembled into three- or four-dimensional maps of protein concentration that can be compared across different cells and conditions. We have developed a method to enable comparison of imaging data from many cells and applied it to investigate actin dynamics in T cell activation. Antigen recognition in T cells by the T cell receptor (TCR) is amplified by engagement of the costimulatory receptor CD28. We imaged actin and eight core actin regulators to generate over a thousand movies of T cells under conditions in which CD28 was either engaged or blocked in the context of a strong TCR signal. Our computational analysis showed that the primary effect of costimulation blockade was to decrease recruitment of the activator of actin nucleation WAVE2 (Wiskott-Aldrich syndrome protein family verprolin-homologous protein 2) and the actin-severing protein cofilin to F-actin. Reconstitution of WAVE2 and cofilin activity restored the defect in actin signaling dynamics caused by costimulation blockade. Thus, we have developed and validated an approach to quantify protein distributions in time and space for the analysis of complex regulatory systems.

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Triple-Color FRET Analysis Reveals Conformational Changes in the WIP-WASp Actin-Regulating Complex

 

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T cell activation by antigens involves the formation of a complex, highly dynamic, yet organized signaling complex at the site of the T cell receptors (TCRs). Srikanth et al. found that the lymphocyte-specific large guanosine triphosphatase of the Rab family CRACR2A-a associated with vesicles near the Golgi in unstimulated mouse and human CD4+ T cells. Upon TCR activation, these vesicles moved to the immunological synapse (the contact region between a T cell and an antigen-presenting cell). The guanine nucleotide exchange factor Vav1 at the TCR complex recruited CRACR2A-a to the complex. Without CRACR2A-a, T cell activation was compromised because of defective calcium and kinase signaling.

More than 60 members of the Rab family of guanosine triphosphatases (GTPases) exist in the human genome. Rab GTPases are small proteins that are primarily involved in the formation, trafficking, and fusion of vesicles. We showed that CRACR2A (Ca2+ release–activated Ca2+ channel regulator 2A) encodes a lymphocyte-specific large Rab GTPase that contains multiple functional domains, including EF-hand motifs, a proline-rich domain (PRD), and a Rab GTPase domain with an unconventional prenylation site. Through experiments involving gene silencing in cells and knockout mice, we demonstrated a role for CRACR2A in the activation of the Ca2+ and c-Jun N-terminal kinase signaling pathways in response to T cell receptor (TCR) stimulation. Vesicles containing this Rab GTPase translocated from near the Golgi to the immunological synapse formed between a T cell and a cognate antigen-presenting cell to activate these signaling pathways. The interaction between the PRD of CRACR2A and the guanidine nucleotide exchange factor Vav1 was required for the accumulation of these vesicles at the immunological synapse. Furthermore, we demonstrated that GTP binding and prenylation of CRACR2A were associated with its localization near the Golgi and its stability. Our findings reveal a previously uncharacterized function of a large Rab GTPase and vesicles near the Golgi in TCR signaling. Other GTPases with similar domain architectures may have similar functions in T cells.

 

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New insights in cancer, cancer immunogenesis and circulating cancer cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Revised 4/20/2016

 

Circulating Tumor Cells Traverse Tiny Vasculature

Clusters of tumor-derived cells can pass through narrow channels that mimic human capillaries, scientists show in vitro and in zebrafish.

By Tanya Lewis | April 18, 2016

http://www.the-scientist.com/?articles.view/articleNo/45883/title/Circulating-Tumor-Cells-Traverse-Tiny-Vasculature

http://www.the-scientist.com/images/News/April2016/tumorclusters1.gif

A stained cluster of cancer cells passes through a 7-μm channel in vitro. PNAS, AU ET AL

Clusters of circulating tumor cells (CTCs) may play a larger role in cancer metastasis than previously thought. Researchers at Massachusetts General Hospital have now shown that these clusters can squeeze through microfluidic channels just 7 microns (μm) in diameter. The team’s findings were published today (April 18) in PNAS.

“There’s a common belief in the field that even single CTCs traversing through capillary beds would destroy the majority of them through physical shearing,” Edward Cho of Spectrum Genomics wrote in an email toThe Scientist. This study “demonstrates new evidence that clusters of CTCs may have a mechanism to prevent shearing as they traverse through small capillaries, and thus may have greater metastatic potential than previously thought,” added Cho, who was not involved in the work.

Most cancer deaths are caused by tumors metastasizing to different organs. Traditionally, clusters of these cells were thought to be too large to pass through capillaries, instead getting stuck and forming blood clots. Yet, more recently, these clusters have been detected in blood drawn from cancer patients. “If they’re so big, how can we find them in blood collection in the arm?” study coauthor Mehmet Toner of Massachusetts General Hospital told The Scientist.

To investigate, Toner and his colleagues created 7-μm-wide microfluidic channels designed to mimic the mechanical properties of human capillaries, and filmed clusters of CTCs—derived from patient blood samples and from cell lines—as they moved through the channels.

As the videos revealed, clusters of 10 to 20 cells loosely disassembled as they entered the channels and moved through the tiny passageways, single-file—like a crowd of people holding hands as they squeeze through a narrow alleyway in a line formation. The cells were “squeezed beyond belief,” said Toner. Upon exiting the channel, the CTCs reassembled into nonlinear clusters.

Next, the researchers sought to validate their model in vivo. They injected human CTC clusters into the bloodstreams of 3-day-old transgenic zebrafish embryos. Zebrafish are a good model for humans because their capillaries are almost identical to those of humans in size and pressure, Toner explained. Again the researchers found that the CTC clusters could traverse the fish capillaries.

http://www.the-scientist.com/images/News/April2016/tumorclusters2.gif

A cluster of four tumor cells elongates and compresses as it travels through a 7-μm microfluidic capillary.PNAS, AU ET AL.

http://www.the-scientist.com/images/News/April2016/tumorclusters3.gif

Clusters of tumor cells (green) moving through a transgenic zebrafish blood vessel (arrows indicate direction of flow through dorsal aorta, caudal vein, and pivot point)PNAS, AU ET AL.

Finally, Toner’s team showed that these CTC clusters could be broken up with certain drugs. The researchers treated clusters with either FAK inhibitor 14, a molecule overproduced by many tumors that inhibits a protein involved in cell-cell adhesion, or the chemotherapy drug paclitaxel, which also weakens cell junctions. When the researchers injected the treated clusters into the microfluidic channels, the clusters broke up into smaller clumps or single CTCs, suggesting a possible avenue for treatment.

“It’s a very interesting paper,” Sanjiv Sam Gambhir of Stanford, a professor of radiology and nuclear medicine who did not take part in the study, told TheScientist. “It’s not known how these clusters of cells end up being the bad [guys] in terms of metastasis. This work very nicely—both through computational modeling, as well as microfluidic devices and zebrafish models—attempts to elucidate this finding.”

However, Gambhir added, the results are still based on models. “Unless you’re doing this in actual human capillaries, you still can’t prove this is what really goes on in humans,” he said.

However, Gambhir added, the results are still based on models. “Unless you’re doing this in actual human capillaries, you still can’t prove this is what really goes on in humans,” he said.

Missing from both the in vitro and zebrafish models was information on how CTC clusters behave in branching capillary beds like those seen in human capillaries, Cho noted. Cho’s team and others have previously shown that CTC clusters may also become stuck in veins, creating clots that can be fatal.

Further, developing treatments based on breaking up these clusters relies on the assumption that CTC clusters have greater metastatic potential than single CTCs, which is still up for debate.

“This study is a good first effort to help us understand how cells might transit through a simplified model of the circulatory system,” Cho wrote in an email, “but until we can model the true complexity of CTCs and CTC clusters traveling through the human circulatory system, we should be cautious not to extrapolate too much in terms of the potential therapeutic potential from the conclusions of studies like these.”

S.H. Au et al., “Clusters of circulating tumor cells traverse capillary-sized vessels,” PNAS,doi:10.1073/pnas.1524448113, 2016.

Clusters of circulating tumor cells traverse capillary-sized vessels

Sam H. Aua,bBrian D. StoreycJohn C. Moored,e,fQin Tangd,e,f, et al.    Sam H. Au,  http://dx.doi.org:/10.1073/pnas.1524448113

Metastasis is responsible for 90% of cancer-related deaths and is driven by tumor cells circulating in blood. However, it is believed that only individual tumor cells can reach distant organs because multicellular clusters are too large to pass through narrow capillaries. Here, we collected evidence by examining clusters in microscale devices, computational simulations, and animals, which suggest that this assumption is incorrect, and that clusters may transit through capillaries by unfolding into single-file chains. This previously unidentified cell behavior may explain why previous experiments reported that clusters were more efficient at seeding metastases than equal numbers of single tumor cells, and has led to a strategy that, if applied clinically, may reduce the incidence of metastasis in patients.

 

Multicellular aggregates of circulating tumor cells (CTC clusters) are potent initiators of distant organ metastasis. However, it is currently assumed that CTC clusters are too large to pass through narrow vessels to reach these organs. Here, we present evidence that challenges this assumption through the use of microfluidic devices designed to mimic human capillary constrictions and CTC clusters obtained from patient and cancer cell origins. Over 90% of clusters containing up to 20 cells successfully traversed 5- to 10-μm constrictions even in whole blood. Clusters rapidly and reversibly reorganized into single-file chain-like geometries that substantially reduced their hydrodynamic resistances. Xenotransplantation of human CTC clusters into zebrafish showed similar reorganization and transit through capillary-sized vessels in vivo. Preliminary experiments demonstrated that clusters could be disrupted during transit using drugs that affected cellular interaction energies. These findings suggest that CTC clusters may contribute a greater role to tumor dissemination than previously believed and may point to strategies for combating CTC cluster-initiated metastasis.
Signal Loop Pulls Healthy Cells into Cancer’s Echo Chamber

http://www.genengnews.com/gen-news-highlights/signal-loop-pulls-healthy-cells-into-cancer-s-echo-chamber/81252618/

In the cellular media environment, some of the most pernicious messaging occurs within tumors, which form a kind of echo chamber that amplifies molecular interactions. These interactions, which support the growth and spread of cancer, occur not only between genetically diverse cancer cells, but also between cancer cells and healthy cells.

That healthy cells should participate in such distorted discourse is disappointing but undeniable, say scientists based at the Institute of Cancer Research (ICR). These scientists report that stromal cells are all too receptive to KRAS signals secreted by cancer cells. Under the influence of oncogenic KRAS, stromal cells secrete a message of their own, one that cancer cells cannot produce themselves, and the stromal cells’ messaging ends up reinforcing the cancer cells’ malignant behavior.

These findings appeared April 14 in the journal Cell, in an article entitled, “Oncogenic KRAS Regulates Tumor Cell Signaling via Stromal Reciprocation.” The article describes how the ICR researchers studied communication networks in cells from a type of pancreatic cancer called pancreatic ductal adenocarcinoma, one of the most deadly forms of cancer.

“By combining cell-specific proteome labeling with multivariate phosphoproteomics, we analyzed heterocellular KRASG12D signaling in pancreatic ductal adenocarcinoma (PDA) cells,” wrote the authors of the Cell article. “Tumor cell KRASG12D engages heterotypic fibroblasts, which subsequently instigate reciprocal signaling in the tumor cells. Reciprocal signaling employs additional kinases and doubles the number of regulated signaling nodes from cell-autonomous KRASG12D.”

Normal KRAS makes occasional signals that tell a cell to divide; but when the gene is mutated, it becomes hyperactive and helps drive cancer cells’ rapid and uncontrolled growth. KRAS is mutated in more than 90% of pancreatic cancer, and in nearly 20% of all cancers.

The authors determined that, “…reciprocal KRASG12D produces a tumor cell phosphoproteome and total proteome that is distinct from cell-autonomous KRASG12D alone. Reciprocal signaling regulates tumor cell proliferation and apoptosis and increases mitochondrial capacity via an IGF1R/AXL-AKT axis.”

In other words, by monitoring reciprocal signaling, the ICR scientists discovered that healthy cells were responding with a totally new message, one propagated via IGF1R/AXL-AKT. This message doubled the capacity for KRAS to drive malignant behavior in cancer cells.

“We now know that tumors are a complex mix of genetically diverse cancer cells and multiple types of healthy cells, all communicating with each other via an intricate web of interactions,” noted Claus Jørgensen, Ph.D., the ICR scientist who led the study and is currently a junior group leader at the Cancer Research UK Manchester Institute. “Untangling this web, and decoding individual signals, is vital to identify which of the multitude of communications are most important for controlling tumor growth and spread.”

“We have identified a key role played by the most commonly mutated gene in cancer in communicating with healthy cells. Blocking its effects could be an effective cancer treatment.”

 

Oncogenic KRAS Regulates Tumor Cell Signaling via Stromal Reciprocation

Christopher J. Tape, Stephanie Ling, Maria Dimitriadi4,…., Douglas A. Lauffenburger, Claus Jørgensen
Cell Apr 2016    http://dx.doi.org/10.1016/j.cell.2016.03.029

In Brief – Cell-specific proteome labeling reveals that oncogenic KRAS stimulates stromal cells to initiate reciprocal signaling back to pancreatic tumor cells, thereby enabling signaling capacity beyond the traditionally studied cell-autonomous pathways.

Highlights

  1.  KRASG12D establishes a reciprocal signaling axis via heterotypic stromal cells
  2.  Reciprocal signaling further regulates tumor cell signaling downstream of KRASG12D
  3.  Reciprocal signaling regulates tumor cell behavior via AXL/ IGF1R-AKT
  4.  Heterocellularity expands tumor cell signaling beyond cellautonomous pathways

Figure thumbnail fx1

http://www.cell.com/cms/attachment/2053320047/2060147607/fx1.jpg

Oncogenic mutations regulate signaling within both tumor cells and adjacent stromal cells. Here, we show that oncogenic KRAS (KRASG12D) also regulates tumor cell signaling via stromal cells. By combining cell-specific proteome labeling with multivariate phosphoproteomics, we analyzed heterocellular KRASG12D signaling in pancreatic ductal adenocarcinoma (PDA) cells. Tumor cell KRASG12D engages heterotypic fibroblasts, which subsequently instigate reciprocal signaling in the tumor cells. Reciprocal signaling employs additional kinases and doubles the number of regulated signaling nodes from cell-autonomous KRASG12D. Consequently, reciprocal KRASG12Dproduces a tumor cell phosphoproteome and total proteome that is distinct from cell-autonomous KRASG12D alone. Reciprocal signaling regulates tumor cell proliferation and apoptosis and increases mitochondrial capacity via an IGF1R/AXL-AKT axis. These results demonstrate that oncogene signaling should be viewed as a heterocellular process and that our existing cell-autonomous perspective underrepresents the extent of oncogene signaling in cancer.

Solid cancers are heterocellular systems containing both tumor cells and stromal cells. Coercion of stromal cells by tumor cell oncogenes profoundly impacts cancer biology (Friedl and Alexander, 2011, Quail and Joyce, 2013) and aberrant tumor-stroma signaling regulates many hallmarks of cancer (Hanahan and Weinberg, 2011). While individual oncogene-driven regulators of tumor-stroma signaling have been identified, the propagation of oncogene-dependent signals throughout a heterocellular system is poorly understood. Consequently, our perspective of oncogenic signaling is biased toward how oncogenes regulate tumor cells in isolation (Kolch et al., 2015).

In a heterocellular cancer, tumor cell oncogenes drive aberrant signaling both within tumor cells (cell-autonomous signaling) and adjacent stromal cells (non-cell-autonomous signaling) (Croce, 2008, Egeblad et al., 2010). As different cell types process signals via distinct pathways (Miller-Jensen et al., 2007), heterocellular systems (containing different cell types) theoretically provide increased signal processing capacity over homocellular systems (containing a single cell type). By extension, oncogene-dependent signaling can theoretically engage additional signaling pathways in a heterocellular system when compared to a homocellular system. However, to what extent activated stromal cells reciprocally regulate tumor cells beyond cell-autonomous signaling is not well understood.

We hypothesized that the expanded signaling capacity provided by stromal heterocellularity allows oncogenes to establish a differential reciprocal signaling state in tumor cells. To test this hypothesis, we studied oncogenic KRAS (KRASG12D) signaling in pancreatic ductal adenocarcinoma (PDA). KRAS is one of the most frequently activated oncogenic drivers in cancer (Pylayeva-Gupta et al., 2011) and is mutated in >90% of PDA tumor cells (Almoguera et al., 1988). PDA is an extremely heterocellular malignancy—composed of mutated tumor cells, stromal fibroblasts, endothelial cells, and immune cells (Neesse et al., 2011). Crucially, the gross stromal pancreatic stellate cell (PSC) expansion observed in the PDA microenvironment is non-cell-autonomously controlled by tumor cell KRASG12D in vivo (Collins et al., 2012, Ying et al., 2012). As a result, understanding the heterocellular signaling consequences of KRASG12D is essential to comprehend PDA tumor biology.

Comprehensive analysis of tumor-stroma signaling requires concurrent measurement of cell-specific phosphorylation events. Recent advances in proteome labeling now permit cell-specific phosphoproteome analysis in heterocellular systems (Gauthier et al., 2013, Tape et al., 2014a). Furthermore, advances in proteomic multiplexing enable deep multivariate phospho-signaling analysis (McAlister et al., 2012, Tape et al., 2014b).

Here, we combine cell-specific proteome labeling, multivariate phosphoproteomics, and inducible oncogenic mutations to describe KRASG12Dcell-autonomous, non-cell-autonomous, and reciprocal signaling across a heterocellular system. This study reveals KRASG12D uniquely regulates tumor cells via heterotypic stromal cells. By exploiting heterocellularity, reciprocal signaling enables KRASG12D to engage oncogenic signaling pathways beyond those regulated in a cell-autonomous manner. Expansion of KRASG12D signaling via stromal reciprocation suggests oncogenic communication should be viewed as a heterocellular process.

….

Whether oncogenes regulate tumor cell signaling via stromal cells is a fundamental question in tumor biology. Using heterocellular multivariate phosphoproteomics, we demonstrate how oncogenic KRAS signals through local non-tumor cells to achieve a differential reciprocal signaling state in the inceptive tumor cells. In PDA, this reciprocal axis supplements oncogenic cell-autonomous signaling to control protein abundance, transcription, mitochondrial activity, proliferation, apoptosis, and colony formation. Reciprocal signaling is the exclusive product of heterocellularity and cannot be achieved by tumor cells alone. These observations imply oncogenes expand their capacity to deregulate cellular signaling via stromal heterocellularity (Figure 7).

Despite the well-established heterocellularity of cancer, our understanding of oncogenic signaling within tumor cells has largely excluded non-tumor cells. We observe that stromal cells approximately double the number of tumor cell signaling nodes regulated by oncogenic KRAS, suggesting both cell-autonomous (internal) and reciprocal (external) stimuli should be considered when defining aberrant oncogenic signaling states. For example, although KRAS is thought to cell-autonomously regulate AKT in PDA (Eser et al., 2014), we show that KRASG12D activates AKT, not cell-autonomously, but reciprocally. As PI3K signaling is essential for PDA formation in vivo (Baer et al., 2014, Eser et al., 2013, Wu et al., 2014) reciprocal signaling may control oncogene-dependent tumorigenesis. Our findings suggest future genetic studies should consider the heterocellular signaling consequences of oncogene/tumor-suppressor deregulation.

The observation that many oncogene-dependent tumor cell signaling nodes require reciprocal activation has important implications for identifying pharmacological inhibitors of oncogene signaling. For example, if PDA tumor cells were screened alone, one would expect MEK, MAPK, and CDK inhibitors to perturb KRASG12D signaling. However, when screened in conjunction with heterotypic stromal cells, our study additionally identified SHH, AKT, and IGF1R/AXL inhibitors as KRASG12D-dependent targets in tumor cells. Inhibitors of signaling specific to reciprocally engaged tumor cells, such as or AKT or IGF1R/AXL, block heterocellular phenotypes (e.g., protein expression, proliferation, mitochondrial performance, and anti-apoptosis), but have little effect on KRASG12D tumor cells alone. An appreciation of reciprocal nodes increases our molecular understanding of drug targets downstream of oncogenic drivers and highlights focal points where reciprocal signals converge (e.g., AKT). These trans-cellular observations reinforce the importance of understanding cancer as a heterocellular disease.

Previous work in PDA tumor cells under homocellular conditions demonstrated cell-autonomous KRASG12D shifts metabolism toward the non-oxidative pentose phosphate pathway (Ying et al., 2012), whereas KRASG12D-ablated cells depend on mitochondrial activity (Viale et al., 2014). Here, we show that heterocellular reciprocal signaling can restore the expression of mitochondrial proteins and subsequently re-establish both mitochondrial polarity and superoxide levels. This suggests KRASG12D regulates non-oxidative flux through cell-autonomous signaling and mitochondrial oxidative phosphorylation through reciprocal signaling. These results provide a unique example of context-dependent metabolic control by oncogenes and reinforce the emerging role of tumor-stroma communication in regulating cancer metabolism (Ghesquière et al., 2014).

In PDA, the stroma has dichotomous pro-tumor (Kraman et al., 2010, Sherman et al., 2014) and anti-tumor (Lee et al., 2014, Rhim et al., 2014) properties. It is becoming increasingly evident that non-cell-autonomously activated stromal cells vary within a tumor and can influence tumors in a non-obvious manner. For example, while vitamin D receptor normalization of stromal fibroblasts improves PDA therapeutic response (Sherman et al., 2014), total stromal ablation increases malignant behavior (Lee et al., 2014, Rhim et al., 2014). Thus, while stromal purging is unlikely to provide therapeutic benefit in PDA, “stromal reprogramming” toward an anti-tumor stroma is now desirable (Brock et al., 2015). Although we describe a largely pro-tumor reciprocal axis, both pro- and anti-tumor stromal phenotypes likely transduce across reciprocal signaling networks. Our work suggests future efforts to therapeutically reprogram the PDA stroma toward anti-tumor phenotypes will require an understanding of reciprocal signaling. In describing the first oncogenic reciprocal axis, this study provides a foundation to measure the cell-cell communication required for anti-tumor stromal reprogramming.

We demonstrate heterocellular multivariate phosphoproteomics can be used to observe reciprocal signaling in vitro. Unfortunately, cell-specific isotopic phosphoproteomics is not currently possible in vivo. To delineate reciprocal signaling in vivo, experimental systems must support manipulation of multiple cell-specific variables and provide cell-specific signaling readouts. Simple pharmacological perturbation of reciprocal nodes (e.g., IGF1R, AXL, AKT, etc.) in existing PDA GEMMs will in principle affect all cell types (e.g., tumor cells, PSCs, immune cells) and cannot provide axis-specific information in vivo. Future in vivo studies of reciprocal signaling will require parallel inducible genetic manipulation (e.g., oncogene activation in cancer cell and/or inhibition of reciprocal node in stromal cell), combined with cell-specific signaling data (e.g., using epithelial tissue mass-cytometry) (Simmons et al., 2015).

We describe KRASG12D reciprocal signaling between PDA tumor cells and PSCs. However, it is likely oncogenic reciprocal signaling occurs across multiple different cell types in the tumor microenvironment. For example, in PDA, FAP+stromal fibroblasts secrete SDF1 that binds tumor cells to suppress T cells (Feig et al., 2013). Our model predicts oncogene signaling expands across several cell types in the tumor microenvironment—including immune cells. Moreover, as oncogenes non-cell-autonomously regulate the stroma in many other tumor types (Croce, 2008), our model predicts oncogenic reciprocal signaling to be a broad phenomenon across all heterocellular cancers. The presented heterocellular multivariate phosphoproteomic workflow now enables future characterization of oncogenic reciprocal signaling in alternative cancer types.

As differentiated cells process signals in unique ways, heterocellularity provides increased signal processing space over homocellularity. We provide evidence that KRASG12D exploits heterocellularity via reciprocal signaling to expand tumor cell signaling space beyond cell-autonomous pathways. Given the frequent heterocellularity of solid tumors, we suspect reciprocal signaling to be a common—albeit under-studied—axis in oncogene-dependent signal transduction.

New Genomic Analysis of Immune Cell Infiltration in Colorectal Cancer

http://www.genengnews.com/gen-news-highlights/new-genomic-analysis-of-immune-cell-infiltration-in-colorectal-cancer/81252616/

Through whole-exome sequencing of colorectal tumors, researchers were able to identify additional driver genes that correlate high neoantigen load with increased lymphocytic infiltration and improved survival. [Giannakis et al., 2016, Cell Reports 15, 1–9]     http://www.genengnews.com/Media/images/GENHighlight/thumb_fx11248133146.jpg

The past several years have seen some exciting results for cancer immunotherapy. However, there remains a fundamental lack of understanding of immune system recognition in various cancers. Many large-scale sequencing efforts have added to our collective knowledge base, but too many of these studies have been deficient in comprehensive epidemiological and demographic information.

Now, researchers at the Dana-Farber Cancer Institute and the Broad Institute of MIT and Harvard report on their findings from a new study, which found that colorectal cancers festooned with tumor-related proteins called neoantigens were likely to be saturated with disease-fighting white blood cells, mainly lymphocytes.

Using several data sets from patients in two large health-tracking studies, the Nurses’ Health Study and the Health Professionals Follow-up Study, investigators performed whole-exome sequencing on colorectal tumor samples from 619 patients—itemizing each DNA base that specifies how cell proteins are to be constructed. This information was merged with data from tests of the immune system’s response to the tumors and with patient clinical data, including length of survival.

“We were looking for genetic features that predict how extensively a tumor is infiltrated by lymphocytes and which types of lymphocytes are present,” explained co-lead study author, Marios Giannakis, M.D., Ph.D., medical oncologist and clinical investigator at the Dana-Farber Gastrointestinal Cancer Treatment Center, and researcher at the Broad Institute of MIT and Harvard. “We found that tumors with a high ‘neoantigen load’—which carry large quantities of neoantigens—tended to be infiltrated by a large number of lymphocytes, including memory T cells, which provide protection against previously encountered infections and diseases. Patients whose tumors had high numbers of neoantigens also survived longer than those with lower neoantigen loads.”

The findings from this study were published recently in Cell Reports in an article entitled “Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.”

Neoantigens are mutated forms of protein antigens, which are found on normal cells. Genetic mutations often cause cancer cells to produce abnormal proteins, some of which get trafficked to the cell surface, where they serve as a red flag to the immune system that something has gone awry with the cell.

“There can be hundreds or thousands of neoantigens on tumor cells,” noted Dr. Giannakis explained. “Only a few of these may actually provoke T cells to infiltrate a tumor. However, the more neoantigens on display, the greater the chance that some of them will spark an immune system response.”

Physicians often take advantage of therapies known as immune checkpoint inhibitors, which work by removing some of the barriers to an immune system attack on cancer. Although these agents have produced astonishing results in some cases, they’re effective only in patients whose immune system has already launched an immune response to cancer. This new study may help investigators identify which patients are most likely to benefit in new clinical trials of immune checkpoint inhibitors by showing that tumors with high antigen loads are apt to be laced with T cells—and therefore able to provoke an immune response.

Interestingly, this new analysis found several often-mutated genes that had not previously been strongly associated with the disease, including BCL9L, RBM10, CTCF, and KLF5, suggesting that they may be valuable targets for new therapies.

“Our study helps shed light on the overall development of colorectal cancer,” Dr. Giannakis remarked. “It also shows the insights that can be gained by integrating molecular research with findings from other areas such as epidemiology and immunology.”

“Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma”

 

Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival
Scott D. Brown,1,2 Rene L. Warren,1 Ewan A. Gibb,1,3 Spencer D. Martin,1,3,4 John J. Spinelli,5,6 Brad H. Nelson,3,4,7 and Robert A. Holt1,3,8,9
Genome Res. 2014 May; 24(5): 743–750.    doi:  10.1101/gr.165985.113

Somatic missense mutations can initiate tumorogenesis and, conversely, anti-tumor cytotoxic T cell (CTL) responses. Tumor genome analysis has revealed extreme heterogeneity among tumor missense mutation profiles, but their relevance to tumor immunology and patient outcomes has awaited comprehensive evaluation. Here, for 515 patients from six tumor sites, we used RNA-seq data from The Cancer Genome Atlas to identify mutations that are predicted to be immunogenic in that they yielded mutational epitopes presented by the MHC proteins encoded by each patient’s autologous HLA-A alleles. Mutational epitopes were associated with increased patient survival. Moreover, the corresponding tumors had higher CTL content, inferred from CD8A gene expression, and elevated expression of the CTL exhaustion markers PDCD1 and CTLA4. Mutational epitopes were very scarce in tumors without evidence of CTL infiltration. These findings suggest that the abundance of predicted immunogenic mutations may be useful for identifying patients likely to benefit from checkpoint blockade and related immunotherapies.

The accumulation of somatic mutations underlies the initiation and progression of most cancers by conferring upon tumor cells unrestricted proliferative capacity (Hanahan and Weinberg 2011). The analysis of cancer genomes has revealed that tumor mutational landscapes (Vogelstein et al. 2013) are extremely variable among patients, among different tumors from the same patient, and even among the different regions of a single tumor (Gerlinger et al. 2012). There is a need for personalized strategies for cancer therapy that are compatible with mutational heterogeneity, and in this regard, immune interventions that aim to initiate or enhance anti-tumor immune responses hold much promise. Therapeutic antibodies and chimeric antigen receptor (CAR) technologies have shown anti-cancer efficacy (Fox et al. 2011), but such antibody-based approaches are limited to cell surface target antigens (Slamon et al. 2001; Coiffier et al. 2002; Yang et al. 2003;Cunningham et al. 2004; Kalos et al. 2011). In contrast, most tumor mutations are point mutations in genes encoding intracellular proteins. Short peptide fragments of these proteins, after intracellular processing and presentation at the cell surface as MHC ligands, can elicit T cell immunoreactivity. Further, the presence of tumor infiltrating lymphocytes (TIL), in particular, CD8+ T cells, has been associated with increased survival (Sato et al. 2005; Nelson 2008; Oble et al. 2009; Yamada et al. 2010; Gooden et al. 2011; Hwang et al. 2012), suggesting that the adaptive immune system can mount protective anti-tumor responses in many cancer patients (Kim et al. 2007; Fox et al. 2011). The antigen specificities of tumor-infiltrating T cells remain almost completely undefined (Andersen et al. 2012), but there are numerous examples of cytotoxic T cells recognizing single amino acid coding changes originating from somatic tumor mutations (Lennerz et al. 2005;Matsushita et al. 2012; Heemskerk et al. 2013; Lu et al. 2013; Robbins et al. 2013;van Rooij et al. 2013; Wick et al. 2014). Thus, the notion that tumor mutations are reservoirs of exploitable neo-antigens remains compelling (Heemskerk et al. 2013). For a mutation to be recognized by CD8+ T cells, the mutant peptide must be presented by MHC I molecules on the surface of the tumor cell. The ability of a peptide to bind a given MHC I molecule with sufficient affinity for the peptide-MHC complex to be stabilized at the cell surface is the single most limiting step in antigen presentation and T cell activation (Yewdell and Bennink 1999). Recently, several algorithms have been developed that can predict which peptides will bind to given MHC molecules (Nielsen et al. 2003; Bui et al. 2005; Peters and Sette 2005; Vita et al. 2010; Lundegaard et al. 2011), thereby providing guidance into which mutations are immunogenic.

The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/) is an initiative of the National Institutes of Health that has created a comprehensive catalog of somatic tumor mutations identified using deep sequencing. As a member of The Cancer Genome Atlas Research Network, our center has generated extensive tumor RNA-seq data. Here, we have used public TCGA RNA-seq data to explore the T cell immunoreactivity of somatic missense mutations across six tumor sites. This type of analysis is challenged not only by large numbers of mutations unique to individual patients, but also by the complexity of personalized antigen presentation by MHC arising from the extreme HLA allelic diversity in the outbred human population. Previous studies have explored the potential immunogenicity of tumor mutations (Segal et al. 2008; Warren and Holt 2010; Khalili et al. 2012), but these have been hampered by small sample size and the inability to specify autologous HLA restriction. Recently, we described a method of HLA calling from RNA-seq data that shows high sensitivity and specificity (Warren et al. 2012). Here, we have obtained matched tumor mutational profiles and HLA-A genotypes from TCGA subjects and used these data to predict patient-specific mutational epitope profiles. The evaluation of these data together with RNA-seq-derived markers of T cell infiltration and overall patient survival provides the first comprehensive view of the landscape of potentially immunogenic mutations in solid tumors.    …..

The results of the present study have clinical implications. We have shown that patients with tumors bearing missense mutations predicted to be immunogenic have a survival advantage (Fig. 3D). These tumors also show evidence of higher CD8+ TIL, which suggests that a number of these mutations might be immunoreactive. The existence of these mutations is encouraging because, in principle, they could be leveraged by personalized therapeutic vaccination strategies or adoptive transfer protocols to enhance anti-tumor immunoreactivity. Likewise, patients with tumors showing naturally immunogenic mutations and associated TIL are potential candidates for treatment with immune modulators such as CTLA4- or PDCD1-targeted antibodies. There is evidence that such therapies are most effective against tumors infiltrated by T cells (Moschos et al. 2006; Hamid et al. 2009). Our results indicate that tumors bearing predicted immunogenic mutations have not only elevated CD8A expression (Fig. 3C) but also elevated expression of CTLA4 and PDCD1 (Fig. 4), reinforcing the notion that these patients may be optimal candidates for immune modulation. Importantly, we observed that tumors with low levels of CD8+ TIL invariably have far fewer immunogenic mutations. Such patients would be better suited to conventional therapy or to immunotherapies (e.g., chimeric antigen receptor modified T cells) that target nonmutated antigens.

  1. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3229261/Apr 12, 2011 Keywords: tumor immune infiltrate, T-cells, cancer prognosis, colon … By conducting genomic and in situ immunostaining on resected tumors from ….. of tumor-infiltrating immune cells correlates with better overall survival.

  2. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234325/May 27, 2011 Keywords: Colorectal cancer, Immune response, T lymphocytes, Microsatellite instability … In CRC, at least 3 distinct pathways of genomic instability have been …. The potential influence of these immunecell infiltrates in CRC on the … in controlling cytolytic activity of CD8+ T cells, inversely correlates to the …   

 

Cancer-Associated Immune Resistance and Evasion of Immune Surveillance in Colorectal Cancer

Gastroenterology Research and Practice
Volume 2016 (2016), Article ID 6261721, 8 pages
http://dx.doi.org/10.1155/2016/6261721


Data from molecular profiles of tumors and tumor associated cells provide a model in which cancer cells can acquire the capability of avoiding immune surveillance by expressing an immune-like phenotype. Recent works reveal that expression of immune antigens (PDL1, CD47, CD73, CD14, CD68, MAC387, CD163, DAP12, and CD15) by tumor cells “immune resistance,” combined with prometastatic function of nonmalignant infiltrating cells, may represent a strategy to overcome the rate-limiting steps of metastatic cascade through (a) enhanced interactions with protumorigenic myeloid cells and escape from T-dependent immune response mediated by CD8+ and natural killer (NK) cells; (b) production of immune mediators that establish a local and systemic tumor-supportive environment (premetastatic niche); (c) ability to survive either in the peripheral blood as circulating tumor cells (CTCs) or at the metastatic site forming a cooperative prometastatic loop with foreign “myeloid” cells, macrophages, and neutrophils, respectively. The development of cancer-specific “immune resistance” can be orchestrated either by cooperation with tumor microenvironment or by successive rounds of genetic/epigenetic changes. Recognition of the applicability of this model may provide effective therapeutic avenues for complete elimination of immune-resistant metastatic cells and for enhanced antitumor immunity as part of a combinatorial strategy.

1. Introduction

Metastasis remains the most significant cause of cancer-associated morbidity and mortality and specific targeting molecules have had limited success in reversing metastatic progression in the clinical setting [13]. Understanding the exact molecular and cellular basis of the events that facilitate cancer metastasis has been difficult so far. Over the past years, a well-accepted theory suggests that genomic alterations of the malignant cells accompanied by the so-called tumor microenvironment “nonmalignant cells” contribute to the metastatic cascade [4, 5]. As such, metastasis is frequently described as the sequential execution of multiple steps. To establish the metastatic tumor, cancer cells have to acquire the traits that enable them to efficiently cooperate with the host stroma and simultaneously avoid antitumor immune response [49]. At early stage of carcinogenesis, tumors appear to be vulnerable because mutant and thus potentially immunogenic tumor cells are being exposed to the immune system, which can recognize them and restrict their growth [10, 11]. This is the case of tumor-infiltrating immune cells particularly CD8+ T cells and NK cells which have the potential to restrict the tumor outgrowth or reject metastatic tumor cells [12, 13]. According to this notion, in most primary tumors, a strong Th1/cytotoxic T cells infiltration correlates with a longer patient’s survival [1214]. Unfortunately, tumor develops multiple mechanisms of evading immune responses, by forming a compromised microenvironment that allows the dissemination of malignant cells in a foreign microenvironment through molecular mechanisms still poorly characterized. A variety of stromal cells, particularly M2-phenotype macrophages and myeloid-derived suppressor cells (MDSCs), are recruited to primary tumors; these not only enhance growth of the primary cancer but also facilitate its metastatic dissemination to distant organs [13, 14]. Notably, cooperative “dialogue” between malignant cells and their microenvironment will go on in the systemic circulation and subsequently in the future metastatic site [1317]. In fact, recent studies have demonstrated that a high systemic inflammatory response, that is, blood neutrophil-lymphocyte ratio (NLR), predicts lower overall survival, higher tumor stage, and a greater incidence of metastasis in multiple tumor types [18, 19]. Therefore, a substantial amount of data suggests a novel dimension of the tumor biology and offers the opportunity to revisit the mechanisms describing evasion of cancer immunosurveillance during the metastatic process. The present review analyses recent studies that elucidate and reinforce the theory by which immune-phenotypic features or “immune resistance” by cancer cells may need to sustain the metastatic cascade and avoid antitumor immune response.

2. Tumor Antigens and Antitumor Immune Response by Effectors of Adaptive Immunity

A decade of studies has emphasized the nature of cancer as a systemic disease remarking a key role of host microenvironment as a critical hallmark. As a result, a new picture of cancer is emerging in particular due to unexpected cross-talk between malignant cells and the immune system [35]. Recent data have expanded the mechanisms of cancer-immune system interactions revealing that every known innate and adaptive immune effector component participates in tumor recognition and control [9, 10]. It is now recognized that in different individuals and with different cancers, at early stage of tumorigenesis, the few cancer cells are detected by NK cells through their encounter with specific ligands on tumor cells [5]. In turn, activation of macrophages and dendritic cells and particularly T and B cells expands production of additional cytokines and further promotes activation of tumor-specific T cells “CD8+ cytotoxic T cells” leading to the generation of immune memory to specific tumor components [1416]. However, in cases where the immune system is not able to eliminate the cancer, a state of equilibrium develops or eventually cancer cells can resist, avoid, or suppress the antitumor immune response, leading to the immune escape and a fully developed tumor (Figure 1) [915]. For example, investigations into the nature of cancer as a genetic disease have suggested two paradigmatic subtypes of colorectal cancer (CRC): chromosomal instability (CIN) and microsatellite instability (MSI), in which the expression of immune-checkpoint proteins can be differentially dysregulated to unleash the potential of the antitumor immune response [11]. In particular, tumors with mismatch-repair deficiency (dMMR) (10–20%) of advanced colorectal cancer tend to have 10 to 100 times more somatic mutations and higher amount of lymphocyte infiltrates than mismatch-repair-proficient colorectal cancers (pMMR), a finding consistent with a stronger antitumor immune response (Figure 1) [11, 20]. According to this notion, recent studies suggest that certain cancer subtypes dMMR CRC with high numbers of somatic mutations are more responsive to PD-1 blockade, a well-known immune-checkpoint inhibitor [20]. In particular, CD8-positive lymphoid infiltrate and membranous PDL1 expression on either tumor cells or tumor-infiltrating lymphocytes at the invasive fronts of the tumor are associated with an improved response to anti-PD-1 therapy in patients with mismatch-repair-deficient cancer [11, 20]. In addition, cancer subtypes with stronger antitumor immune responses (immunogenic) are characterized by surface-exposed calreticulin or heat shock protein 90 (HSP90), which serve as a powerful mobilizing signal to the immune system in the context of damage-associated molecular patterns (DAMPs) [17]. As danger signals, DAMPs accompanied by subversion MHC Class I and II antigens on the plasma membrane of cancer cells appear to be characteristic of stressed or injured cells and can act as adjuvant signals to enhance antitumor immunity mediated by the innate immune system [17]. As described in this review, unfortunately, the large majority of human tumors can suppress the immune system to enhance their survival, rendering them invisible to cytotoxic T lymphocytes through a variety of mechanisms. Furthermore, in most cases, tumor-infiltrating immune cells differentiate into cells that promote each step of the tumor progression supporting ability of cancer cells to invade and survive in foreign organs. In addition, the intricate network of malignant and immune components represents a prominent obstacle to the effects of therapeutic agents.

………..

High-resolution genomic analysis: the tumor-immune interface comes into focus

Jonathan J Havel1 and Timothy A Chan1,2*
Havel and Chan     Genome Biology (2015) 16:65      http://dx.doi.org:/10.1186/s13059-015-0631-3

A genomic analysis of heterogeneous colorectal tumor samples has uncovered interactions between immunophenotype and various aspects of tumor biology, with implications for informing the choice of immunotherapies for specific patients and guiding the design of personalized neoantigen-based vaccines.

Please see related article: http://dx.doi.org/10.1186/s13059-015-0620-6

Immunotherapy is a promising new approach for treating human malignancies. Approximately 20% of melanoma and lung cancer patients receiving immune checkpoint inhibitors show responses [1,2]. Current major challenges include identification of patients most likely to respond to specific therapies and elucidation of novel targets to treat those who do not. To address these problems, a detailed understanding of the dynamic interactions between tumors and the immune system is required. In a new study, Zlatko Trajanoski and colleagues [3] describe a powerful approach to dissecting these issues through high-resolution analysis of patient genomic data. This study represents a significant advance over previous work from this group, which defined 28 immune-cell-type gene expression signatures and identified specific cell types as prognostic indicators in colorectal cancer (CRC) patients [4]. Here, the authors [3] integrate genomic analyses of CRC tumor molecular phenotypes, predicted antigenicity (called the ‘antigenome’), and immune-cell infiltration derived from multiple independent cohorts to gain refined insights into tumor-immune system interactions.

Not all tumor-infiltrating lymphocytes are created equal

Past studies have used immune-staining techniques to determine associations between a limited set of infiltrating immune cells and patient survival [5] or tumor molecular phenotype [6]. Here, the authors [3] use gene set enrichment analysis (GSEA) of immune cell expression signatures to ascertain associations of 28 immune-cell populations with patient survival and tumor molecular phenotypes. Effector memory CD8+ and CD4+ T cells, natural killer cells, and activated dendritic cells are significantly associated with improved overall survival. Interestingly, although the authors’ previous work found no significant prognostic value of regulatory T cells (Tregs) or myeloid-derived suppressor cells (MDSCs) [4], negative associations of these cell types with overall survival are among the strongest relationships observed in the current study. It is possible that variations in sample collection and preparation may have contributed to this discrepancy. The conclusions, supported by the numerous animal studies demonstrating the importance of cell-mediated immunosuppression, are substantially strengthened by a much larger cohort size used in this study.

Another important observation is the association of specific immune cell subsets with CRC tumor stage and molecular phenotypes as classified by mutation rate, microsatellite instability, and methylation status. This knowledge will be crucial in determining which types of immunotherapy are most likely to benefit individual patients. Interestingly, although hypermutated microsatellite-unstable tumors show strong enrichment of adaptive immune cells, similar enrichment is notably lacking in the small population of hypermutated microsatellite-stable tumors. This raises an intriguing question of whether and how microsatellite instability/mismatch repair may independently shape immune responses. Furthermore, Trajanoski and colleagues [3] find that tumor-infiltrating lymphocytes transition from an adaptive to an innate immunophenotype with increasing tumor stage. This raises an interesting issue of whether immunotherapies that depend on the adaptive immune response can be effective in later stage CRC tumors.

Diversity of tumor antigens

In addition to characterizing immune components involved in tumor immune responses, it is equally important to identify and understand the tumor-associated antigens that elicit these responses, called the ‘antigenome’. The authors [3] analyze RNA-seq and genomic data to identify two types of tumor antigens in CRC – non-mutated cancer germline antigens that are aberrantly overexpressed, and neoantigens, which are generated from non-synonymous somatic mutations. Importantly, the authors [3] find that cancer-germline antigens are highly shared among patients and are independent of molecular and immune phenotype. In contrast, neoantigens are enriched in the hypermutated microsatellite-unstable phenotype tumors and rarely shared among patients. These results imply a heightened importance of neoantigens in comparison to cancer-germline antigens [7]. In addition, similar analytical methods have recently been applied to identify functional neoantigens in human melanoma and cholangiocarcinoma [810]. An emerging theme of these studies is that the in vitro validation rate for predicted neoantigens is relatively low; however, it is unclear whether this is due to limited sensitivity of functional assays or epigenetic silencing to circumvent immunoediting, or whether the number of immunogenic neoantigens is in fact small. Interestingly, Trajanoski and colleagues [3] find a modest yet significant decrease in neoantigen frequency with increasing tumor stage. Considering the concomitant decrease in adaptive immune cell infiltration, it is tempting to speculate that this phenomenon reflects immunoediting of critical neoepitopes during tumor progression. Furthermore, the authors find an association, albeit not statistically significant, between increased neoantigen burden and improved patient survival. This finding complements a recent report [9] showing that whereas neoantigen burden roughly predicts survival of anti-CTLA-4-treated melanoma patients, a collection of consensus neoepitope motifs is strongly associated with patient survival. It will be interesting to see if future studies can determine the effect of CRC neoantigen burden in the setting of immunotherapy, and answer the questions of whether an analogous signature of prognostic neoepitope motifs exists for CRC, and whether there are any similarities between substring signatures of different tumor types.

………

Not Your Average Circulating Tumor Cells   

Translational Scientists Profile Cancer Cells That Have Gone on the Lam

GEN Apr 15, 2016 (Vol. 36, No. 8)    http://www.genengnews.com/gen-articles/not-your-average-circulating-tumor-cells/5737/

 

  • For most malignant tumors, morbidity and mortality are, to a great extent, the result of metastatic dissemination, as opposed to the presence of the primary tumor.

    The existence of circulating tumor cells, which can be shed into the circulation by primary or metastatic malignancies, was first recognized almost 150 years ago, and their diagnostic and therapeutic values have been increasingly appreciated during the last few decades.

    One of the unique characteristics of circulating tumor cells is that they are in a fundamentally different environment from that established in either the primary tumor or the metastatic one. Although circulating tumor cells can be kept in place so that they can be assessed, the usual technique—immobilization to a solid surface—tends to yield distorted results. Free-floating cells are molecularly and functionally distinct from immobilized cells. For example, nonadhering breast cancer cells were shown to have tubulin-based microtentacles that shape their dynamic behavior, including their aggregation, retention in organs, or interaction with the endothelium.

    “These microtentacles are very hard to study because they depolymerize when cells bind either an endothelial cell or another tumor cell,” says Christopher M. Jewell, Ph.D., assistant professor of bioengineering  at the University of Maryland College Park. “Cells that form microtumors undergo massive mechanochemical and phenotypic changes as compared to when they are floating or circulating.”

    Characterizing circulating tumor cells, then, seems to amount to capturing the substance of freedom, a task that sounds self-defeatingly paradoxical—or at least fraught with difficulties. Overcoming difficulties, however, would likely be worth the effort. Two areas that immediately benefit from the characterization of circulating tumor cells are diagnostics and therapeutics.

    Capturing and analyzing circulating tumor cells opens not only the possibility of diagnosing patients earlier and more accurately, but also the potential for identifying new approaches to targeting malignancies. “Many groups are working on important technologies to capture circulating tumor cells,” informs Dr. Jewell. “We’re working on new technologies to analyze these populations.”

  • Floating in Place

    To address the existing gap in characterizing the biology of free-floating cancer cells, Dr. Jewell and collaborators in the University of Maryland laboratory of physiologist Stuart Martin, Ph.D., have designed an unusual  microfluidic device. It can spatially immobilize free-floating tumor cells while maintaining their free-floating characteristics.

    In this microfluidic device, polyelectrolyte multilayers inhibit the attachment of cells to multiwall plates, allowing their free-floating functional and morphological characteristics to be visualized and studied. Lipid tethers incorporated into the device interact with the cell membrane and allow cells to remain loosely attached and spatially localized, offering the possibility to perform applications such as real-time imaging and drug screening.

    “We are trying to understand what the signaling changes are in individual circulating tumors cells that are not nucleating into a tumor,” explains Dr. Jewell, “as compared to cells that contact enough cells and nucleate to form a tumor.”

    Surface tethering of circulating tumor cells also provides the opportunity to capture arrays of tumor cells; to introduce a perturbation such as a drug or a change in flow rate or mechanical properties; and then to collect the same individual cells that had already been imaged. In these cells, morphological changes can be correlated with genomic or proteomic information, providing an opportunity to dynamically understand how the mechanochemical properties of the cells change in response to external perturbations.

    “Our collaborators,” notes Dr. Jewell, “are also developing algorithms to quantify some of the features of microtentacles and convert visual information into quantitative metrics.”

  • Filterless Filters

    At the University of California, Los Angeles, Dino Di Carlo, Ph.D., and colleagues have developed High-Throughput Vortex Chip (Vortex-HT) technology, which uses parallel microfluidic vortex chambers to accumulate the larger circulating tumor cells from flowing blood. Vortex-HT reportedly generates less contamination with white blood cells than other technologies and isolates cells in a smaller output volume.

    Early techniques to capture circulating tumor cells have taken advantage of cell size differences, leading to the development of filtration-based approaches. This was followed, more recently, by the emergence of inertial microfluidic-based approaches, of which vortex technology is one example.

    “We think of vortex technology as a filterless filter,” says Dino Di Carlo, Ph.D., professor of bioengineering and director of the Cancer Nanotechnology Program at the Jonsson Comprehensive Cancer Center of the University of California, Los Angeles. “There aren’t any structures that are smaller than the cell types, but cells are still isolated based on size.”

    Dr. Di Carlo and colleagues recently developed the High-Throughput Vortex Chip (Vortex HT), an improved microfluidic technology that allows the label-free, size-based enrichment and concentration of rare cells. The strategy involves minimal pretreatment steps, reducing cell damage, and allows an approximately 8 mL vial of blood to be processed within 15–20 minutes.

    “With this approach,” asserts Dr. Di Carlo, “we can concentrate cells from any volume to about 100 µL.”

    Circulating tumor cells can then be used for subsequent steps, such as real-time imaging or immunostaining. The capture efficiency, up to 83%, is slightly lower than with Dean flow fractionation and CTC-iChip, but Vortex HT generates much less contamination with white blood cells than other technologies and isolates cells in a smaller output volume.

    Along with circulating tumor cells, another promising noninvasive biomarker is provided by circulating tumor DNA. Such DNA can be detected in the plasma or serum of many cancer patients as a result of the active or passive release of nucleic acid from apoptotic or necrotic tumor cells.

    While circulating tumor DNA can be used to dynamically collect information about specific mutations, and provides advantages for some applications, it is not powered to offer certain types of information that can be captured only from circulating tumor cells. For example, it cannot provide details about cellular morphology or protein expression and localization. Also, it cannot enable investigators to perform proteomic profiling in parallel with genomic profiling.

    These are not the only situations in which circulating DNA serves as a poor substitute for circulating tumor cells. “Another example,” notes Dr. Di Carlo, occurs with “applications that involve a drug screen that seeks to determine whether cells are sensitive or resistant to a particular compound.” Additionally, for certain cancers that have no dominant mutations, or for which mutations are not well known, circulating tumor DNA cannot provide the information that can be interrogated from profiling circulating tumor cells.

    • Insights beyond Counting

      “The field started off by enumerating circulating tumor cells as a potential biomarker,” says David T. Miyamoto, M.D., Ph.D., assistant professor of radiation oncology at Harvard Medical School and the Massachusetts General Hospital (MGH). “It is currently moving toward performing detailed molecular analyses of these circulating tumor cells and using them as a form of liquid biopsy that allows us to gain insights into the molecular biology of the tumor itself.”

      The Circulating Tumor Cell Center at MGH, led by Daniel Haber, M.D., Ph.D., and Mehmet Toner, Ph.D., has developed three generations of microfluidic technology. The technology of the first two generations captured circulating tumor cells on microfluidic surfaces. The technology of the third generation, known as CTC-iChip technology, introduces the unique capability—isolating cells in solution. Once the circulating tumor cells are captured or isolated, notes Dr. Miyamoto, they can be subjected to “a variety of sophisticated molecular analyses.”

      In a recent study using the CTC-iChip technology, Dr. Miyamoto and colleagues performed single-cell RNA sequencing. The investigators used 77 circulating tumor cells isolated by microfluidic enrichment from 13 patients.

      “The goal of this work was to use the circulating tumor cell technology to identify potential resistance mechanisms in metastatic castration-resistant prostate cancer,” explains Dr. Miyamoto. In patients who were undergoing therapy with an androgen receptor inhibitor, the retrospective analysis of their circulating tumor cells revealed that the noncanonical Wnt signaling pathway may play a role in resistance to therapy.

      “We need to validate the findings in larger patient cohorts,” concludes Dr. Miyamoto. “But this proof-of-concept study shows that detailed molecular analyses of liquid biopsy samples can be used to identify potentially clinically relevant mechanisms of resistance that can then be exploited to guide patient care.”

    • Variable to the Last

      Morphotek, a biopharmaceutical company that specializes in the development of protein and antibody products through the use of gene-evolution technology, has developed the ApoStream device, which uses continuous field-flow-assist and dielectrophoresis technology to isolate and recover circulating tumor cells from the blood of cancer patients. In a recent study in Biomarker Insights, Morphotek scientists described how they interrogated ApoStream-isolated circulating tumor cells by employing laser-scanning cytometry using highly selective antibodies. The scientists detected folate receptor alpha (FRα) expression in CK+/CD45 cells isolated from lung cancer, as indicated in these representative images.

      “Most of the work on circulating tumor cells has been done in late-stage cancers to direct therapeutic interventions,” says Daniel J. O’Shannessy, Ph.D., head of translational medicine and diagnostics at Morphotek. “Even in late-stage cancers, there is a great deal of variability with respect to the numbers of cells shed for a cancer type but especially between cancer types.”

      Many studies correlated the presence of circulating tumor cells with prognosis in several cancers, including breast, lung, and colorectal malignancies. However, one of the challenges associated with analyzing circulating tumor cells is that not every cancer releases them into the circulation. Also, even among cancers that do, not every cancer generates a lot of these cells.

      For example, as estimated using current techniques, ovarian cancers do not appear to shed as many circulating tumor cells as several other malignancies. “Another challenge is that existing technologies are often limited by sensitivity much more than by specificity,” cautions Dr. O’Shannessy. This limitation has the potential to make interpatient comparisons, and even the longitudinal follow-up of patients, particularly difficult.

      Previously, investigators at Morphotek described ApoStream®, a device that uses continuous field-flow-assist and dielectrophoresis technology to isolate and recover circulating tumor cells from the blood of cancer patients. In a recent study, Dr. O’Shannessy and colleagues used laser-scanning cytometry and highly selective antibodies to identify folate receptor alpha-positive cells from circulating tumor cells that had been isolated using the ApoStream technology.

      This proof-of-principle study was able to detect folate receptor alpha-positive cells in patients with breast cancer, ovarian cancer, and non-small cell lung adenocarcinoma, but not in patients with squamous cell lung cancer. These findings supporting previous findings that were made using the respective primary or metastatic tumors.

      The study demonstrated the utility of following the enrichment and identification of circulating tumor cells with immunofluorescence staining for a specific tumor marker. This combination of approaches emerges as a valuable noninvasive strategy for differentiating among tumor types. It can also be used to examine heterogeneous cell populations within tumors, particularly when tissue samples are not available.

    • Outliers among Outliers

      “We know that circulating tumor cells are present in cancer patients, but we have a limited understanding of the prognostic significance of their presence, or how to identify the ones that have more metastatic potential,” says Shana O. Kelley, Ph.D., professor of biochemistry at the University of Toronto. “These are questions we are trying to address to obtain functional information.”

      Recently, Dr. Kelley and colleagues described a new molecular approach based on a fluidic chip that captures circulating tumor cells using two-dimensional sorting. At a first stage, DNA aptamers specific to cell-surface markers bound to magnetic nanoparticles are used to capture circulating tumor cells. Subsequently, at a second stage, the corresponding antisense oligonucleotides are used to release the cells, enabling two-dimensional cell sorting.

      In a proof-of-concept experiment, Dr. Kelley and colleagues illustrated the strength of this approach in isolating cellular subpopulations that exhibit different phenotypes. Also, the investigators validated their results using an invasion assay.

      “Progress in working on the biology of circulating tumor cells motivates us to make devices to collect information about markers much more readily,” declares Dr. Kelley. “We hope this will provide information about outcomes and prognosis.”

 

 

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Programmed Cell Death and Cancer Therapy

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Programmed Death: A Cat and Mouse Game

 http://www.cancernetwork.com/blog/programmed-death-cat-and-mouse-game

Prologue: The world of cancer care has been shaken up by the news that patients with hard-to-treat tumors benefit from a new type of immunotherapy, called checkpoint inhibition. A key receptor, called programmed death 1 (PD-1), is charged with suppressing the ability of activated T cells and other immune cells to destroy cancer cells, all in the name of preventing damage to normal tissue via autoimmunity. When PD-1 receptors on T cells bind with PD-L1 and PD-L2, complimentary receptors expressed on tumor cells, the immune response (call it the assassination of the cell) is checked and the tumor lives on. The anti PD-1 monoclonal antibodies nivolumab and pembrolizumab keep PD-L1 from turning off T cells, which has produced durable responses in several tumor types including melanoma, lung cancer, and renal cell carcinoma and represents a new hope for many.

Oncologists are excited to relay this news to patients, but is there a way to explain this without putting everyone in the room to sleep? Well, I like to use analogies to make seemingly complicated mechanisms easier to understand and the PD-1/PD-L1 relationship has inspired several colorful examples, to wit:

“Think of T cells as killers that use photographs to identify individual bad guys. Their weakness is that they will not act if the intended victim shakes their hand first. The bad guys used to be born without arms, but over time they evolved to grow arms and hands, thus avoiding elimination. The antibodies are boxing gloves that cover the hands of the T cells. Goodbye, bad guys.”

“Think of T cells as cats specially trained to eliminate mice wherever they hide. Their only weakness is if they smell catnip they will roll over and purr like idiots instead of doing their job. The mice then develop special glands that secrete catnip, thus pacifying the kitties. Solution: plug up the cats’ noses with nivolumab or pembrolizumab. Sayonara, Mr. Mouse.”

“Think of T cells as a fire sprinkler system designed to activate when a metal plug is heated to its melting point, releasing water from a pipe. The fire then emits a toxin that coats the fusible metal, keeping it below its melting point. By fitting a protective shield around the plug we block the toxic molecules and allow the plug to melt in a fire. The shield is the monoclonal antibody against PD-1 and thus the fire is successfully extinguished.”

This is getting exhausting, so I think I will stop, but don’t you agree that the concept of checkpoint inhibition lends itself to a plethora of metaphors? Now for the next lesson: how to explain chimeric antigen receptor T-cell therapy to patients. Hold on—I think I need to explain it to myself first.

 

As an clinical immunologist, i agree with the concept, looks pretty straightforward but definitely much more complex as our immune system work like a network. I would appreciate clinical trial data with statistical significance.

 

Much more confusing. Most people understand simplified concepts.

“Some cancer cells turn off your immune systems ability to recognise them. These drugs ramp up the immune system and prevent the cancer cells from hiding. This allows your cells to attack and kill cancer cells”

If i think patient seem to have better ability to understand I say “the drugs block the “off switch” that cancer cells use to escape their detection. This turns your immune systems ability to attack and kill cancer cells back on”

I haven’t had one patient that has looked confused since.

 

HDAC Inhibitors Enhance Immunotherapy Efficacy in Lung Cancer

http://www.oncotherapynetwork.com/lung-cancer-targets/hdac-inhibitors-enhance-immunotherapy-efficacy-lung-cancer

Histone deacetylase (HDAC) inhibitors like romidepsin might improve the efficacy of programmed cell death-1 (PD-1) blockade in lung cancer, suggest preclinical findings reported in the journal Clinical Cancer Research.

Most lung cancer patients’ tumors do not respond to immune checkpoint blockade agents like those that target PD-1. One possible mechanism underlying tumor resistance to PD-1 blockade is the failure of sufficient numbers of T cells to infiltrate tumor tissue.

Hypothesizing that upregulating T-cell chemokine expression and thereby T-cell infiltration of tumors would improve PD-1 blockade’s efficacy against lung tumors, the research team went hunting for FDA-approved oncology agents that induce chemokine expression. Screening 97 approved agents, they found one class that did: HDAC inhibitors.

The HDAC-inhibiting agent romidepsin significantly increased T-cell tumor infiltration and impacted lung tumor growth in mouse models, the team reported—and when romidepsin was subsequently combined with PD-1 blockade in several lung tumor models, the combination showed greater antitumor activity than either agent on its own.

“These results suggest that combination of HDAC inhibitors with PD-1 blockade represent a promising strategy for lung cancer treatment,” said senior study author Amer A. Beg, PhD, of the Moffitt Cancer Center’s Immunology Program, in a news release.

Romidepsin and other HDAC inhibitors have already been approved by the FDA for use against lymphoma and other hematologic cancers, Dr. Beg noted.

The combination will next be tested in several clinical trials, including a study of patients diagnosed with non-small cell lung cancer (NSCLC) at Moffitt Cancer Center.

 

HDAC inhibitors enhance T cell chemokine expression and augment response to PD-1 immunotherapy in lung adenocarcinoma

Hong Zheng1,  weipeng zhao2Cihui Yan3Crystina C Watson4,…., Brian Ruffell13, and Amer A Beg4,*

Clin Cancer Res March 10, 2016; http://dx.doi.org:/10.1158/1078-0432.CCR-15-2584

Purpose: A significant limitation of checkpoint blockade immunotherapy is the relatively low response rate (e.g. ~20% with PD-1 blockade in lung cancer). In this study, we tested whether strategies which increase T cell infiltration to tumors can be efficacious in enhancing immunotherapy response. Experimental Design: We performed an unbiased screen to identify FDA-approved oncology agents with ability to enhance T cell chemokine expression with the goal of identifying agents capable of augmenting immunotherapy response. Identified agents were tested in multiple lung tumor models as single agents and in combination with PD-1 blockade. Additional molecular and cellular analysis of tumors was used to define underlying mechanisms. Results: We found that histone deacetylase (HDAC) inhibitors (HDACi) increased expression of multiple T cell chemokines in cancer cells, macrophages and T cells. Using the HDACi romidepsin in vivo, we observed increased chemokine expression, enhanced T cell infiltration, and T cell-dependent tumor regression. Importantly, romidepsin significantly enhanced the response to PD-1 blockade immunotherapy in multiple lung tumor models, including nearly complete rejection in two models. Combined romidepsin and PD-1 blockade also significantly enhanced activation of tumor-infiltrating T cells. Conclusions: These results provide evidence for a novel role of HDACs in modulating T cell chemokine expression in multiple cell types. In addition, our findings indicate that pharmacological induction of T cell chemokine expression represents a conceptually novel approach for enhancing immunotherapy response. Finally, these results suggest that combination of HDAC inhibitors with PD-1 blockade represents a promising strategy for lung cancer treatment.

 

Cancer Cell Survival Driven by Novel Metabolic Pathway

http://www.genengnews.com/gen-news-highlights/cancer-cell-survival-driven-by-novel-metabolic-pathway/81252584/

Researchers have identified a novel metabolic pathway that helps cancer cells thrive in conditions that are lethal to normal cells. [National Cancer Institute, NIH] http://www.genengnews.com/Media/images/GENHighlight/thumb_28216_large1422477191.jpg

Being attached to the extracellular matrix (ECM) provides cells with numerous advantages for survival, for instance, receiving much needed growth stimuli. However, for malignant cells to function, they must overcome their anchorage-dependent growth—a scenario that is associated with increased production of reactive oxygen species (ROS) and altered glucose metabolism.

Now, researchers at the Children’s Medical Center Research Institute at UT Southwestern (CRI) believe they have uncovered a novel metabolic pathway that helps cancer cells thrive in conditions that would otherwise be lethal to healthy cells.

“It’s long been thought that if we could target tumor-specific metabolic pathways, it could lead to effective ways to treat cancer,” explained senior study author Ralph DeBerardinis, M.D., Ph.D.,  associate professor, and director of CRI’s Genetic and Metabolic Disease Program. “This study finds that two very different metabolic processes are linked in a way that is specifically required for cells to adapt to the stress associated with cancer progression.”

This new study describes an alternate version of two well-known metabolic pathways, the pentose phosphate pathway (PPP) and the Krebs cycle, to defend against ROS that destroy cells via oxidative stress.

The findings from this study were published recently in Nature in an article entitled “Reductive Carboxylation Supports Redox Homeostasis During Anchorage-Independent Growth.”

Previous work from Dr. DeBerardinis’ laboratory found that the Krebs cycle, a series of chemical reactions that cells use to generate energy, could reverse itself under certain conditions to nourish cancer cells.

Dr. DeBerardinis also noted that cells “are dependent on matrix attachment to receive growth-promoting signals and to regulate their metabolism in a way that supports cell growth, proliferation, and survival.” Detachment from the matrix results in a sudden increase in ROS that is lethal to normal cells. Yet, cancer cells seem to have evolved workaround.

A landmark study from 2009 elucidated that healthy cells were destroyed when detached from the ECM. Moreover, in the same study, investigators found that inserting an oncogene into a normal cell caused it to behave like a cancer cell and survive detachment.

“Another Nature study, this one from CRI Director Dr. Sean Morrison’s laboratory in November 2015, found that the rare skin cancer cells that were able to detach from the primary tumor and successfully metastasize to other parts of the body had the ability to keep ROS levels from getting dangerously high,” Dr. DeBerardinis remarked.

Dr. DeBerardinis and his team worked from the premise that the two findings were pieces of the same puzzle and that a crucial part of the picture seemed to be missing.

It had been well known that the PPP was an important source of nicotine adenine dinucleotide phosphate (NADPH), which provides a source of reducing electrons to scavenge ROS; however, the PPP produces NADPH in the cytosol, whereas the ROS are generated primarily in another subcellular structure called the mitochondria.

“If you think of ROS as fire, then NADPH is like the water used by cancer cells to douse the flames,” Dr. DeBerardinis noted.  But how could NADPH from the PPP help deal with the stress of ROS produced in an entirely different part of the cell? “What we did was to discover how this happens.”

The CRI team was able to demonstrate that cancer cells use a “piggybacking” system to carry the reducing electron from the PPP into the mitochondria. This movement involves an unusual reaction in the cytosol that transfers reducing equivalents from NADPH to a molecule called citrate, similar to a reversed reaction of the Krebs cycle.The citrate then enters the mitochondria and stimulates another pathway that results in the release of reducing electrons to produce NADPH right at the location of ROS creation, allowing the cancer cells to survive and grow without the benefit of matrix attachment.

“We knew that both the PPP and Krebs cycle provide metabolic benefits to cancer cells. But we had no idea that they were linked in this unusual fashion,” Dr. DeBerardinis stated. “Strikingly, normal cells were unable to transport NADPH by this mechanism, and died as a result of the high ROS levels.”

The researchers stressed that their findings were based on cultured cell models and more research will be necessary to test the role of the pathway in living organisms.

“We are particularly excited to test whether this pathway is required for metastasis because cancer cells need to survive in a matrix-detached state in the circulation in order to metastasize,” Dr. DeBerardinis concluded.

 

Reductive carboxylation supports redox homeostasis during anchorage-independent growth

Lei JiangAlexander A. ShestovPamela SwainChendong Yang, …., Brian P. DrankaBenjamin Schwartz & Ralph J. DeBerardinis

Nature(2016)      http://dx.doi.org:/10.1038/nature17393

Cells receive growth and survival stimuli through their attachment to an extracellular matrix (ECM)1. Overcoming the addiction to ECM-induced signals is required for anchorage-independent growth, a property of most malignant cells2. Detachment from ECM is associated with enhanced production of reactive oxygen species (ROS) owing to altered glucose metabolism2. Here we identify an unconventional pathway that supports redox homeostasis and growth during adaptation to anchorage independence. We observed that detachment from monolayer culture and growth as anchorage-independent tumour spheroids was accompanied by changes in both glucose and glutamine metabolism. Specifically, oxidation of both nutrients was suppressed in spheroids, whereas reductive formation of citrate from glutamine was enhanced. Reductive glutamine metabolism was highly dependent on cytosolic isocitrate dehydrogenase-1 (IDH1), because the activity was suppressed in cells homozygous null for IDH1 or treated with an IDH1 inhibitor. This activity occurred in absence of hypoxia, a well-known inducer of reductive metabolism. Rather, IDH1 mitigated mitochondrial ROS in spheroids, and suppressing IDH1 reduced spheroid growth through a mechanism requiring mitochondrial ROS. Isotope tracing revealed that in spheroids, isocitrate/citrate produced reductively in the cytosol could enter the mitochondria and participate in oxidative metabolism, including oxidation by IDH2. This generates NADPH in the mitochondria, enabling cells to mitigate mitochondrial ROS and maximize growth. Neither IDH1 nor IDH2 was necessary for monolayer growth, but deleting either one enhanced mitochondrial ROS and reduced spheroid size, as did deletion of the mitochondrial citrate transporter protein. Together, the data indicate that adaptation to anchorage independence requires a fundamental change in citrate metabolism, initiated by IDH1-dependent reductive carboxylation and culminating in suppression of mitochondrial ROS.

 

Liquid Biopsy Accurately Detects Mutations in Advanced NSCLC

http://www.oncotherapynetwork.com/lung-cancer/liquid-biopsy-accurately-detects-mutations-advanced-nsclc#sthash.bEPxRkAq.dpuf

Droplet digital polymerase chain reaction (ddPCR)-based plasma genotyping—referred to as liquid biopsy—exhibited perfect specificity in identifying EGFR and KRAS mutations in patients with advanced non–small-cell lung cancer (NSCLC), according to the results of a study published in JAMA Oncology.

“We see plasma genotyping as having enormous potential as a clinical test, or assay—a rapid, noninvasive way of screening a cancer for common genetic fingerprints, while avoiding the challenges of traditional invasive biopsies,” said senior author, Geoffrey Oxnard, MD, thoracic oncologist and lung cancer researcher at Dana-Farber and Brigham and Women’s Hospital, in a press release. “Our study was the first to demonstrate prospectively that a liquid biopsy technique can be a practical tool for making treatment decisions in cancer patients.”

According to the press release, the test proved so reliable in the study that the Dana-Farber/Brigham and Women’s Cancer Center this week became the first medical facility in the country to offer it to all patients with NSCLC, either at the time of first diagnosis or of relapse following previous treatment.

Oxnard and colleagues enrolled 180 patients with advanced NSCLC. Patients were either newly diagnosed with the disease (n = 120) or had acquired resistance to prior EGFR kinase inhibitors (n = 60) and were planned for rebiopsy. Patients underwent initial blood sampling and immediate plasma ddPCR screening for EGFR exon 19 deletion, L858R, the EGFR T790M acquired resistance mutation, or KRAS G12X. In addition, patients underwent biopsy for tissue genotyping used to compare the accuracy of ddPCR.

Among the enrolled patients, 80 had EGFR exon19/L858R mutations, 35 had T790M mutations and 25 had KRAS G12X mutations. The median test turnaround time for liquid biopsy was 3 days. In comparison, the median turnaround time for tissue genotyping was 12 days for newly diagnosed patients and 27 days for patients with acquired EGFR inhibitor resistance.

“This long turnaround time is due largely to the practical reality that many patients with newly diagnosed NSCLC require a repeat biopsy to obtain tissue for genotyping, as do all patients with acquired resistance,” the researchers noted.

The liquid biopsy showed 100% positive predictive value for detecting EGFR 19 deletion, L858R, and KRAS mutations. However, it had only a positive predictive value of 79% for T790M mutations. The sensitivity of the test was lower. ddPCR had a sensitivity of 82% for EGFR 19 deletion, 74% for L858R, 77% for T790M, and 64% for KRAS.

The researchers pointed out that “a key limitation of plasma ddPCR is that although this method is adept at rapidly detecting specific targetable mutations, it cannot easily detect copy number alterations and rearrangements. The ddPCR panel assessed in this study thus cannot currently detect targetable alterations in either ALK or ROS1,” two other common mutations in NSCLC.

In an editorial that accompanied the article, P. Mickey Williams, PhD, of Frederick National Laboratory for Cancer Research, and Barbara A. Conley, MD, from the National Cancer Institute, questioned whether or not these results, and the rapid turnaround time for liquid biopsy, could be replicated widely by other institutions.

“However, if this performance were generally applicable, this would indeed be an advance in clinical care, reducing the proportion of patients requiring biopsy, at least in the resistance setting,” Williams and Conley wrote.

“This study is a step in the right direction of preparing needed clinical validation for the use of ctDNA for detection and serial monitoring of clinically relevant tumor mutations. Owing to low sensitivity and high positive predictive value and specificity, this approach is probably best suited for detection of resistance mutations and for serial plasma testing to assess treatment response, and should not replace tumor biopsy assessment for initial treatment decision-making,” they concluded.

 

Prospective Validation of Rapid Plasma Genotyping for the Detection of EGFR and KRAS Mutations in Advanced Lung Cancer

Adrian G. Sacher, MD1,2; Cloud Paweletz, PhD3; Suzanne E. Dahlberg, PhD4,5; Ryan S. Alden, BSc1; Allison O’Connell, BSc3; Nora Feeney, BSc3; Stacy L. Mach, BA1; Pasi A. Jänne, MD, PhD1,2,3; Geoffrey R. Oxnard, MD1,2

JAMA Oncol. Published online April 07, 2016.    http://dx.doi.org:/10.1001/jamaoncol.2016.0173

Importance  Plasma genotyping of cell-free DNA has the potential to allow for rapid noninvasive genotyping while avoiding the inherent shortcomings of tissue genotyping and repeat biopsies.

Objective  To prospectively validate plasma droplet digital PCR (ddPCR) for the rapid detection of common epidermal growth factor receptor (EGFR) and KRAS mutations, as well as the EGFR T790M acquired resistance mutation.

Design, Setting, and Participants  Patients with advanced nonsquamous non–small-cell lung cancer (NSCLC) who either (1) had a new diagnosis and were planned for initial therapy or (2) had developed acquired resistance to an EGFR kinase inhibitor and were planned for rebiopsy underwent initial blood sampling and immediate plasma ddPCR for EGFR exon 19 del, L858R, T790M, and/or KRAS G12X between July 3, 2014, and June 30, 2015, at a National Cancer Institute–designated comprehensive cancer center. All patients underwent biopsy for tissue genotyping, which was used as the reference standard for comparison; rebiopsy was required for patients with acquired resistance to EGFR kinase inhibitors. Test turnaround time (TAT) was measured in business days from blood sampling until test reporting.

Main Outcomes and Measures  Plasma ddPCR assay sensitivity, specificity, and TAT.

Results  Of 180 patients with advanced NSCLC (62% female; median [range] age, 62 [37-93] years), 120 cases were newly diagnosed; 60 had acquired resistance. Tumor genotype included 80 EGFR exon 19/L858R mutants, 35 EGFR T790M, and 25 KRASG12X mutants. Median (range) TAT for plasma ddPCR was 3 (1-7) days. Tissue genotyping median (range) TAT was 12 (1-54) days for patients with newly diagnosed NSCLC and 27 (1-146) days for patients with acquired resistance. Plasma ddPCR exhibited a positive predictive value of 100% (95% CI, 91%-100%) for EGFR 19 del, 100% (95% CI, 85%-100%) for L858R, and 100% (95% CI, 79%-100%) for KRAS, but lower for T790M at 79% (95% CI, 62%-91%). The sensitivity of plasma ddPCR was 82% (95% CI, 69%-91%) for EGFR 19 del, 74% (95% CI, 55%-88%) for L858R, and 77% (95% CI, 60%-90%) for T790M, but lower for KRAS at 64% (95% CI, 43%-82%). Sensitivity for EGFR or KRAS was higher in patients with multiple metastatic sites and those with hepatic or bone metastases, specifically.

Conclusions and Relevance  Plasma ddPCR detected EGFR and KRAS mutations rapidly with the high specificity needed to select therapy and avoid repeat biopsies. This assay may also detect EGFR T790M missed by tissue genotyping due to tumor heterogeneity in resistant disease.

Plasma genotyping uses tumor-derived cell-free DNA (cfDNA) to allow for rapid noninvasive genotyping of tumors. This technology is currently poised to transition into a treatment decision-making tool in multiple cancer types. It is particularly relevant to the treatment of advanced non–small-cell lung cancer (NSCLC), in which therapy hinges on rapid and accurate detection of targetable epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and ROS1 alterations.1– 6Plasma genotyping is capable of circumventing many limitations of standard tissue genotyping including slow turnaround time (TAT), limited tissue for testing, and the potential for failed biopsies. It may be particularly useful in directing the rapid use of new targeted therapies for acquired resistance in advanced EGFR-mutant NSCLC, where the need for a repeat biopsy to test for resistance mechanisms has amplified the inherent limitations of traditional genotyping.7,8

The need to carefully validate the test characteristics of each of the myriad individual plasma genotyping assays before use in clinical decision making is paramount. We have previously reported the development of a quantitative droplet digital polymerase chain reaction (ddPCR)-based assay for the detection of EGFR kinase mutations andKRAS codon 12 mutations in plasma.9 The detection of these mutations has the potential to guide treatment by either facilitating targeted therapy with an EGFR tyrosine kinase inhibitor (TKI) or ruling out the presence of other potentially targetable alterations in the case of KRAS.5 Alternative platforms including Cobas, peptide nucleic acid–mediated PCR, multiplexed next-generation sequencing (NGS), high-performance liquid chromatography, and Scorpion–amplified refractory mutation system have also been examined in retrospective analyses of patient samples.10– 22 The test characteristics of these assays have been variable and may be attributable to differences in testing platforms, as well as the retrospective nature of these studies, their smaller size, and the timing of blood collection with respect to disease progression and therapy initiation. The absence of reliable prospective data on the use of specific plasma genotyping assays in advanced NSCLC has left key aspects of its utility largely undefined and slowed its uptake as a tool for clinical care in patients with both newly diagnosed NSCLC and EGFR acquired resistance.

To our knowledge, we have conducted the first prospective study of the use of ddPCR-based plasma genotyping for the detection of EGFR and KRAS mutations. This study was performed in the 2 settings where we anticipate clinical adoption of this assay: (1) patients with newly diagnosed advanced NSCLC and (2) those with acquired resistance to EGFR kinase inhibitors. The primary aim of this study was to prospectively evaluate the feasibility and accuracy of this assay for the detection ofEGFR/KRAS mutations in patients with newly diagnosed NSCLC and EGFR T790M in patients with acquired resistance in a clinical setting. Additional end points included test TAT and the effect of sample treatment conditions on test accuracy.

Key Points
  • Question What is the sensitivity, specificity, turnaround time, and robustness of droplet digital polymerase chain reaction (ddPCR)-based plasma genotyping for the rapid detection of targetable genomic alterations in patients with advanced non–small-cell lung cancer (NSCLC)?

  • Findings In this study of 180 patients with advanced NSCLC (120 newly diagnosed, 60 with acquired resistance to epidermal growth factor receptor [EGFR] kinase inhibitors), plasma genotyping exhibited perfect specificity (100%) and acceptable sensitivity (69%-80%) for the detection of EGFR-sensitizing mutations with rapid turnaround time (3 business days). Specificity was lower for EGFR T790M (63%), presumably secondary to tumor heterogeneity and false-negative tissue genotyping.

  • Meaning The use of ddPCR-based plasma genotyping can detect EGFR mutations with the rigor necessary to direct clinical care. This assay may obviate repeated biopsies in patients with positive plasma genotyping results.

CYP3A7*1C Allele Associated With Poor Outcomes in CLL, Breast, and Lung Cancer

 http://www.oncotherapynetwork.com/breast-cancer-targets/cyp3a71c-allele-associated-poor-outcomes-cll-breast-and-lung-cancer#sthash.7uiD8XFD.dpuf

Patients with the CYP3A7*1C allele suffer higher rates of cancer progression and mortality, possibly because of worse outcomes among patients treated with chemotherapy drugs that are broken down by the enzyme encoded by CYP3A7, according to authors of a retrospective study published in the journal Cancer Research.

“We found that individuals with breast cancer, lung cancer, or CLL [chronic lymphocytic leukemia] who carry one or more copy of the CYP3A7*1C allele tend to have worse outcomes,” said Olivia Fletcher, PhD, a senior investigator at the Breast Cancer Now Toby Robins Research Centre at the Institute of Cancer Research in London, England, in an American Association for Cancer Research (AACR) news release.

Approximately 8% of cancer patients harbor the CYP3A7*1C allele, the coauthors noted. For these patients, it is possible that standard chemotherapy with CYP3A substrates “may not be optimal,” they cautioned.

The team analyzed DNA samples from 1,008 patients with breast cancer, 1,128 patients with lung cancer, and 347 patients with CLL. They found that the CYP3A7*1C-associated single nucleotide polymorphism (SNP) rs45446698 is associated with increased breast cancer mortality (hazard ratio [HR] 1.74; P = .03), all-cause mortality among patients with lung cancer (HR 1.43; P = .009), and progression of CLL (HR 1.62; P = .03). The rs45446698 SNP is one of seven SNPs that form the CYP3A7*1C allele.

The CYP3A7*1C allele is expressed in adults, whereas other variants of CYP3A7 are expressed during fetal development. CYP3A7 encodes an enzyme that degrades estrogen and testosterone, and some anticancer drugs.

“We also found borderline evidence of a statistical interaction between the CYP3A7*1C allele, treatment of patients with a cytotoxic agent that is a CYP3A substrate, and clinical outcome (P = .06),” they noted.

“Even though we did not see a statistically-significant difference when stratifying patients by treatment with a CYP3A7 substrate, the fact that we see the same effect in three very different cancer types suggests to me that it is more likely to be something to do with treatment than the disease itself,” commented Dr. Fletcher. “However, we are looking at ways of replicating these results in additional cohorts of patients and types of cancer, as well as overcoming the limitations of this study.”

Limitations included the retrospective nature of the study and the absence of data on how quickly individual patients metabolized chemotherapeutic agents, she said.

 

Cytochrome P450 AlleleCYP3A7*1C Associates with Adverse Outcomes in Chronic Lymphocytic Leukemia, Breast, and Lung Cancer

Nichola Johnson1,2Paolo De Ieso3Gabriele Migliorini4,….., Gillian Ross12Richard S. Houlston, and Olivia Fletcher1,2,*

Cancer Res March 10, 2016; http://dx.doi.org:/10.1158/0008-5472.CAN-15-1410

CYP3A enzymes metabolize endogenous hormones and chemotherapeutic agents used to treat cancer, thereby potentially affecting drug effectiveness. Here, we refined the genetic basis underlying the functional effects of a CYP3A haplotype on urinary estrone glucuronide (E1G) levels and tested for an association betweenCYP3A genotype and outcome in patients with chronic lymphocytic leukemia (CLL), breast, or lung cancers. The most significantly associated SNP was rs45446698, an SNP that tags the CYP3A7*1Callele; this SNP was associated with a 54% decrease in urinary E1G levels. Genotyping this SNP in 1,008 breast cancer, 1,128 lung cancer, and 347 CLL patients, we found that rs45446698 was associated with breast cancer mortality (HR, 1.74; P = 0.03), all-cause mortality in lung cancer patients (HR, 1.43; P = 0.009), and CLL progression (HR, 1.62; P= 0.03). We also found borderline evidence of a statistical interaction between the CYP3A7*1C allele, treatment of patients with a cytotoxic agent that is a CYP3A substrate, and clinical outcome (Pinteraction = 0.06). The CYP3A7*1C allele, which results in adult expression of the fetal CYP3A7 gene, is likely to be the functional allele influencing levels of circulating endogenous sex hormones and outcome in these various malignancies. Further studies confirming these associations and determining the mechanism by which CYP3A7*1C influences outcome are required. One possibility is that standard chemotherapy regimens that include CYP3A substrates may not be optimal for the approximately 8% of cancer patients who are CYP3A7*1C carriers. Cancer Res; 76(6); 1–9. ©2016 AACR.

 

​Specific Form of CYP3A7 Gene Associated With Poor Outcomes for Patients With Several Cancer Types

3/10/2016

PHILADELPHIA — Among patients with breast cancer, lung cancer, or chronic lymphocytic leukemia (CLL), those who had a specific form of the CYP3A7 gene (CYP3A7*1C) had worse outcomes compared with those who did not have CYP3A7*1C, and this may be related to how the patients metabolize, or break down, the therapeutics used to treat them, according to a study published in Cancer Research, a journal of the American Association for Cancer Research.

“The CYP3A7 gene encodes an enzyme that breaks down all sorts of naturally occurring substances—such as sex steroids like estrogen and testosterone—as well as a wide range of drugs that are used in the treatment of cancer,” saidOlivia Fletcher, PhD, a senior investigator at the Breast Cancer Now Toby Robins Research Centre at The Institute of Cancer Research in London. “The CYP3A7 gene is normally turned on in an embryo and then turned off shortly after a baby is born, but individuals who have one or more copy of the CYP3A7*1C form of the gene [the CYP3A7*1C allele] turn on their CYP3A7 gene in adult life.

“We found that individuals with breast cancer, lung cancer, or CLL who carry one or more copy of the CYP3A7*1C allele tend to have worse outcomes,” continued Fletcher. “One possibility is that these patients break down the drugs that they are given to treat their cancer too fast. However, further independent studies that replicate our findings in larger numbers of patients and rule out biases are needed before we could recommend any changes to the treatment that cancer patients with the CYP3A7*1C allele receive.”

To find out whether the CYP3A7*1C allele was associated with outcome for patients with breast cancer, lung cancer, or CLL, Fletcher and colleagues analyzed DNA samples from 1,008 breast cancer patients, 1,142 patients with lung cancer, and 356 patients with CLL for the presence of a single nucleotide polymorphism (SNP), rs45446698. Fletcher explained that a SNP is a type of genetic variant and that because of the way that we inherit our genetic material from our parents, we tend to inherit clusters of genetic variants. She went on to say that rs45446698 is one of seven SNPs that cluster together, forming the CYP3A7*1C allele.

The researchers found that among the breast cancer patients, rs45446698 (and, therefore, the CYP3A7*1C allele) was associated with a 74 percent increased risk of breast cancer mortality. Among the lung cancer patients, it was associated with a 43 percent increased risk of death from any cause, and among the CLL patients, it was associated with a 62 percent increased risk of disease progression.

Patients who were treated with a chemotherapeutic broken down by CYP3A7 tended to have worse outcomes compared with those treated with other chemotherapeutics, but the difference was not statistically significant.

“Even though we did not see a statistically significant difference when stratifying patients by treatment with a CYP3A7 substrate, the fact that we see the same effect in three very different cancer types suggests to me that it is more likely to be something to do with treatment than the disease itself,” said Fletcher. “However, we are looking at ways of replicating these results in additional cohorts of patients and types of cancer, as well as overcoming the limitations of this study.”

Fletcher explained that the main limitation of the study is that the researchers used samples and clinical information collected for other studies. Therefore, they did not have the same clinical information for each patient, and the samples were collected at different time points and for patients treated with various chemotherapeutics. She also noted that the team were not able to determine how quickly the patients broke down the therapeutics they received as treatment.

The study was supported by Breast Cancer Now, Leukaemia and Lymphoma Research (now known as Bloodwise), Cancer Research UK, the Medical Research Council, the Cridlan Trust, the Helen Rollason Cancer Charity, and Sanofi-Aventis. Funding for the authors’ institutions was received from the National Health Service of the United Kingdom. Fletcher declares no conflicts of interest.

 

Liquid Biopsy for NSCLC

‘Liquid biopsy’ blood test accurately detects key genetic mutations in most common form of lung cancer, study finds.

http://www.technologynetworks.com/Diagnostics/news.aspx?ID=190276

A simple blood test can rapidly and accurately detect mutations in two key genes in non-small cell lung tumors, researchers at Dana-Farber Cancer Institute and other institutions report in a new study – demonstrating the test’s potential as a clinical tool for identifying patients who can benefit from drugs targeting those mutations.

The test, known as a liquid biopsy, proved so reliable in the study that Dana-Farber/Brigham and Women’s Cancer Center (DF/BWCC) expects to offer it soon to all patients with non-small cell lung cancer (NSCLC), either at the time of first diagnosis or of relapse following previous treatment.

…….

“Our study was the first to demonstrate prospectively that a liquid biopsy technique can be a practical tool for making treatment decisions in cancer patients. The trial was such a success that we are transitioning the assay into a clinical test for lung cancer patients at DF/BWCC.”

The study involved 180 patients with NSCLC, 120 of whom were newly diagnosed, and 60 of whom had become resistant to a previous treatment, allowing the disease to recur. Participants’ cell-free DNA was tested for mutations in the EGFR and KRAS genes, and for a separate mutation in EGFR that allows tumor cells to become resistant to front-line targeted drugs. The test was performed with a technique known as droplet digital polymerase chain reaction (ddPCR), which counts the individual letters of the genetic code in cell-free DNA to determine if specific mutations are present. Each participant also underwent a conventional tissue biopsy to test for the same mutations. The results of the liquid biopsies were then compared to those of the tissue biopsies.

The data showed that liquid biopsies returned results much more quickly. The median turnaround time for liquid biopsies was three days, compared to 12 days for tissue biopsies in newly diagnosed patients and 27 days in drug-resistant patients.

Liquid biopsy was also found to be highly accurate. In newly diagnosed patients, the “predictive value” of plasma ddPCR was 100 percent for the primary EGFR mutation and the KRAS mutation – meaning that a patient who tested positive for either mutation was certain to have that mutation in his or her tumor. For patients with the EGFR resistance mutation, the predictive value of the ddPCR test was 79 percent, suggesting the blood test was able to find additional cases with the mutation that were missed using standard biopsies.

Prospective Validation of Rapid Plasma Genotyping for the Detection of EGFRand KRAS Mutations in Advanced Lung Cancer

Adrian G. Sacher, MD1,2; Cloud Paweletz, PhD3; Suzanne E. Dahlberg, PhD, et al.       JAMA Oncol. Published online April 07, 2016.  http://dx.doi.org::/10.1001/jamaoncol.2016.0173

Importance  Plasma genotyping of cell-free DNA has the potential to allow for rapid noninvasive genotyping while avoiding the inherent shortcomings of tissue genotyping and repeat biopsies.

Objective  To prospectively validate plasma droplet digital PCR (ddPCR) for the rapid detection of common epidermal growth factor receptor (EGFR) and KRAS mutations, as well as the EGFR T790M acquired resistance mutation.

Design, Setting, and Participants  Patients with advanced nonsquamous non–small-cell lung cancer (NSCLC) who either (1) had a new diagnosis and were planned for initial therapy or (2) had developed acquired resistance to an EGFR kinase inhibitor and were planned for rebiopsy underwent initial blood sampling and immediate plasma ddPCR for EGFR exon 19 del, L858R, T790M, and/or KRAS G12X between July 3, 2014, and June 30, 2015, at a National Cancer Institute–designated comprehensive cancer center. All patients underwent biopsy for tissue genotyping, which was used as the reference standard for comparison; rebiopsy was required for patients with acquired resistance to EGFR kinase inhibitors. Test turnaround time (TAT) was measured in business days from blood sampling until test reporting.

Main Outcomes and Measures  Plasma ddPCR assay sensitivity, specificity, and TAT.

Results  Of 180 patients with advanced NSCLC (62% female; median [range] age, 62 [37-93] years), 120 cases were newly diagnosed; 60 had acquired resistance. Tumor genotype included 80 EGFR exon 19/L858R mutants, 35 EGFR T790M, and 25 KRASG12X mutants. Median (range) TAT for plasma ddPCR was 3 (1-7) days. Tissue genotyping median (range) TAT was 12 (1-54) days for patients with newly diagnosed NSCLC and 27 (1-146) days for patients with acquired resistance. Plasma ddPCR exhibited a positive predictive value of 100% (95% CI, 91%-100%) for EGFR 19 del, 100% (95% CI, 85%-100%) for L858R, and 100% (95% CI, 79%-100%) for KRAS, but lower for T790M at 79% (95% CI, 62%-91%). The sensitivity of plasma ddPCR was 82% (95% CI, 69%-91%) for EGFR 19 del, 74% (95% CI, 55%-88%) for L858R, and 77% (95% CI, 60%-90%) for T790M, but lower for KRAS at 64% (95% CI, 43%-82%). Sensitivity for EGFR or KRAS was higher in patients with multiple metastatic sites and those with hepatic or bone metastases, specifically.

Conclusions and Relevance  Plasma ddPCR detected EGFR and KRAS mutations rapidly with the high specificity needed to select therapy and avoid repeat biopsies. This assay may also detect EGFR T790M missed by tissue genotyping due to tumor heterogeneity in resistant disease.

 

In this prospective study, we demonstrate the highly specific and rapid nature of plasma genotyping. No false-positive test results were seen for driver mutations inEGFR or KRAS, and TAT from when the specimen was obtained to result was a matter of days. This assay exhibited 100% positive predictive value for the detection of these mutations. Sensitivity was more modest and was directly correlated with both number of metastatic sites and the presence of liver or bone metastases. This newly demonstrated relationship is likely related to increased cfDNA shed in the setting of more extensive disease where tumor cfDNA shed is the chief driver of assay sensitivity and determines its upper limit. The characteristics of plasma ddPCR prospectively demonstrated in this study were similar or improved compared with previous retrospective reports of other cfDNA genotyping assays.10– 13,15,16,24,25 These retrospective studies are smaller, frequently examined a mix of tumor types and/or stages, and lack the careful prospective design needed to demonstrate the readiness of this technology to transition to a tool for selecting therapy. Studies that use retrospective samples from clinical trials that enrolled only EGFR-mutant patients are further limited by an inability to both blind laboratory investigators to tissue genotype and to generalize their assay test characteristics to a genetically heterogeneous real-world patient population.11 These differences and the multiple platforms examined previously have led to variable test characteristics and uncertainty regarding the clinical application of these technologies. This study is the first to prospectively demonstrate the ability of a ddPCR-based plasma genotyping assay to rapidly and accurately detect EGFR and KRAS mutations in a real-world clinical setting with the rigor necessary to support the assertion that use of this assay is capable of directing clinical care.

Even with a diagnostic sensitivity of less than 100%, such a rapid assay with 100% positive predictive value carries the potential for immense clinical utility. The 2- to 3-day TAT contrasts starkly with the 27-day TAT for tumor genotyping seen in patients needing a new tumor biopsy. This long TAT is due largely to the practical reality that many patients with newly diagnosed NSCLC require a repeat biopsy to obtain tissue for genotyping, as do all patients with acquired resistance. Consider the case of 1 study participant, an octogenarian with metastatic NSCLC who had developed acquired resistance to erlotinib with painful bone metastases (Figure 3). Due to the patient’s age and comorbidities, significant concerns existed about the risks of a biopsy and further systemic therapy. A plasma sample was obtained, and within 24 hours ddPCR demonstrated 806 copies/mL of EGFR T790M. A confirmatory lung biopsy was performed, which confirmed EGFR T790M. Treatment with a third-generation EGFR kinase inhibitor, osimertinib mesylate, was subsequently initiated and the patient had a partial response to therapy that was maintained for more than 1 year. The potential of this technology to obviate repeated biopsy in both patients with newly diagnosed NSCLC with insufficient tissue, as well as patients with acquired resistance, is considerable.

A key limitation of plasma ddPCR is that although this method is adept at rapidly detecting specific targetable mutations, it cannot easily detect copy number alterations and rearrangements. The ddPCR panel assessed in this study thus cannot currently detect targetable alterations in either ALK or ROS1. This limitation may potentially be addressed by using targeted NGS of cfDNA for broad, multiplexed detection of complex genomic alterations including ALK and ROS1 rearrangements, although this method is potentially slower than ddPCR-based methods and has been less thoroughly evaluated.23 The potential exists to use these technologies in tandem in advanced NSCLC to facilitate rapid initiation of therapy. Tissue genotyping and repeated biopsy would be specifically used to direct therapy in cases in which plasma genotyping was uninformative due to limitations of assay sensitivity. This approach would be particularly useful in cases of EGFR acquired resistance in which a repeated biopsy for T790M testing could be avoided entirely in many patients. Beyond detecting targetable alterations in order to drive therapy, the identification of nontargetable oncogenic drivers such as KRAS mutations that preclude the presence of other targetable alterations may guide a clinician to rapidly initiate alternative therapies such as chemotherapy or immunotherapy.5 The finding that assay sensitivity is highest in patients with more extensive metastatic disease suggests that those patients most in need of rapid treatment initiation would also be least likely to have false-negative results.

One surprising result of our study was evidence of recurrent false-positive results forEGFR T790M in patients with acquired resistance, despite no false-positive test results for other mutations studied. The sensitivity of the EGFR T790M assay was comparable to that of the EGFR sensitizing mutation assays and similarly related to both disease burden and the presence of liver or bone metastases, which are likely predictive of increased tumor cfDNA shed. We hypothesize that the lower assay specificity is due to the genomic heterogeneity whereby the T790M status of the biopsied site is not representative of all metastatic sites in a patient, a phenomenon supported by mounting evidence in the acquired resistance setting.26,27 This is consistent with the finding that a minority of patients with apparently EGFR T790M tissue-negative disease respond to therapy with third-generation EGFR kinase inhibitors.7,8,28 These observations raise questions regarding the fallibility of tissue-based genotyping as the reference standard for T790M status. The use of plasma genotyping to detect EGFR T790M thus has great potential to identify patients who would benefit from newly approved third-generation EGFR kinase inhibitors but would be unable to access them based on falsely negative tissue genotyping results. Indeed, plasma genotyping may allow more reliable assessment of both T790M status as well as the mechanisms of resistance across all sites of a heterogeneous cancer as opposed to a tissue biopsy and is likely to be an essential tool for future trials targeting drug resistance. The potential to avoid a repeat biopsy entirely in patients in whom plasma ddPCR detects T790M further strengthens the utility of this technology, although a repeat biopsy would still be needed in patients with uninformative plasma ddPCR due to limitations with respect to assay sensitivity.

This study also examined the potential of the quantitative nature of ddPCR-based plasma genotyping to allow for the early prediction of treatment response. Distinct patterns of change in mutant allele copy number were observed as early as 2 weeks after treatment and were similar to those reported in other tumor types.19,20 We hypothesize that these distinct patterns of change in this study will correlate with specific patterns of radiographic response and emergence of acquired resistance and plan to report these data once mature. The observed differences in treatment discontinuation rates observed in this study comparing patients with complete resolution of detectable mutant cfDNA with those with incomplete resolution support this hypothesis. The use of this technology to monitor disease status in real time has potential utility for both routine clinical care, as well as use as an integrated biomarker in early-phase clinical trials.10

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Inflammatory Disorders: Articles published @ pharmaceuticalintelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

This is a compilation of articles on Inflammatory Disorders that were published 

@ pharmaceuticalintelligence.com, since 4/2012 to date

There are published works that have not been included.  However, there is a substantial amount of material in the following categories:

  1. The systemic inflammatory response
    https://pharmaceuticalintelligence.com/2014/11/08/introduction-to-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/
    https://pharmaceuticalintelligence.com/2014/11/09/summary-and-perspectives-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/
    https://pharmaceuticalintelligence.com/2015/12/19/neutrophil-serine-proteases-in-disease-and-therapeutic-considerations/
    https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/
    https://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
    https://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
    https://pharmaceuticalintelligence.com/2012/07/08/zebrafish-provide-insights-into-causes-and-treatment-of-human-diseases/
    https://pharmaceuticalintelligence.com/2016/01/25/ibd-immunomodulatory-effect-of-retinoic-acid-il-23il-17a-axis-correlates-with-the-nitric-oxide-pathway/
    https://pharmaceuticalintelligence.com/2015/11/29/role-of-inflammation-in-disease/
    https://pharmaceuticalintelligence.com/2013/03/06/can-resolvins-suppress-acute-lung-injury/
    https://pharmaceuticalintelligence.com/2015/02/26/acute-lung-injury/
  2. sepsis
    https://pharmaceuticalintelligence.com/2012/10/20/nitric-oxide-and-sepsis-hemodynamic-collapse-and-the-search-for-therapeutic-options/
  3. vasculitis
    https://pharmaceuticalintelligence.com/2015/02/26/acute-lung-injury/
    https://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
    https://pharmaceuticalintelligence.com/2012/11/20/the-potential-for-nitric-oxide-donors-in-renal-function-disorders/
  4. neurodegenerative disease
    https://pharmaceuticalintelligence.com/2013/02/27/ustekinumab-new-drug-therapy-for-cognitive-decline-resulting-from-neuroinflammatory-cytokine-signaling-and-alzheimers-disease/
    https://pharmaceuticalintelligence.com/2016/01/26/amyloid-and-alzheimers-disease/
    https://pharmaceuticalintelligence.com/2016/02/15/alzheimers-disease-tau-art-thou-or-amyloid/
    https://pharmaceuticalintelligence.com/2016/01/26/beyond-tau-and-amyloid/
    https://pharmaceuticalintelligence.com/2015/12/10/remyelination-of-axon-requires-gli1-inhibition/
    https://pharmaceuticalintelligence.com/2015/11/28/neurovascular-pathways-to-neurodegeneration/
    https://pharmaceuticalintelligence.com/2015/11/13/new-alzheimers-protein-aicd-2/
    https://pharmaceuticalintelligence.com/2015/10/31/impairment-of-cognitive-function-and-neurogenesis/
    https://pharmaceuticalintelligence.com/2014/05/06/bwh-researchers-genetic-variations-can-influence-immune-cell-function-risk-factors-for-alzheimers-diseasedm-and-ms-later-in-life/
  5. cancer immunology
    https://pharmaceuticalintelligence.com/2013/04/12/innovations-in-tumor-immunology/
    https://pharmaceuticalintelligence.com/2016/01/09/signaling-of-immune-response-in-colon-cancer/
    https://pharmaceuticalintelligence.com/2015/05/12/vaccines-small-peptides-aptamers-and-immunotherapy-9/
    https://pharmaceuticalintelligence.com/2015/01/30/viruses-vaccines-and-immunotherapy/
    https://pharmaceuticalintelligence.com/2015/10/20/gene-expression-and-adaptive-immune-resistance-mechanisms-in-lymphoma/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  6. autoimmune diseases: rheumatoid arthritis, colitis, ileitis, …
    https://pharmaceuticalintelligence.com/2016/02/11/intestinal-inflammatory-pharmaceutics/
    https://pharmaceuticalintelligence.com/2016/01/07/two-new-drugs-for-inflammatory-bowel-syndrome-are-giving-patients-hope/
    https://pharmaceuticalintelligence.com/2015/12/16/contribution-to-inflammatory-bowel-disease-ibd-of-bacterial-overgrowth-in-gut-on-a-chip/
    https://pharmaceuticalintelligence.com/2016/02/13/cytokines-in-ibd/
    https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/
    https://pharmaceuticalintelligence.com/2014/10/14/autoimmune-disease-single-gene-eliminates-the-immune-protein-isg15-resulting-in-inability-to-resolve-inflammation-and-fight-infections-discovery-rockefeller-university/
    https://pharmaceuticalintelligence.com/2015/03/01/diarrheas-bacterial-and-nonbacterial/
    https://pharmaceuticalintelligence.com/2016/02/11/intestinal-inflammatory-pharmaceutics/
    https://pharmaceuticalintelligence.com/2014/01/28/biologics-for-autoimmune-diseases-cambridge-healthtech-institutes-inaugural-may-5-6-2014-seaport-world-trade-center-boston-ma/
    https://pharmaceuticalintelligence.com/2015/11/19/rheumatoid-arthritis-update/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
    https://pharmaceuticalintelligence.com/2012/09/13/tofacitinib-an-oral-janus-kinase-inhibitor-in-active-ulcerative-colitis/
    https://pharmaceuticalintelligence.com/2013/03/05/approach-to-controlling-pathogenic-inflammation-in-arthritis/
    https://pharmaceuticalintelligence.com/2013/03/05/rheumatoid-arthritis-risk/
    https://pharmaceuticalintelligence.com/2012/07/08/the-mechanism-of-action-of-the-drug-acthar-for-systemic-lupus-erythematosus-sle/
  7. T cells in immunity
    https://pharmaceuticalintelligence.com/2015/09/07/t-cell-mediated-immune-responses-signaling-pathways-activated-by-tlrs/
    https://pharmaceuticalintelligence.com/2015/05/14/allogeneic-stem-cell-transplantation-9-2/
    https://pharmaceuticalintelligence.com/2015/02/19/graft-versus-host-disease/
    https://pharmaceuticalintelligence.com/2014/10/14/autoimmune-disease-single-gene-eliminates-the-immune-protein-isg15-resulting-in-inability-to-resolve-inflammation-and-fight-infections-discovery-rockefeller-university/
    https://pharmaceuticalintelligence.com/2014/05/27/immunity-and-host-defense-a-bibliography-of-research-technion/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
    https://pharmaceuticalintelligence.com/2013/04/14/immune-regulation-news/

Proteomics, metabolomics and diabetes

https://pharmaceuticalintelligence.com/2015/11/16/reducing-obesity-related-inflammation/

https://pharmaceuticalintelligence.com/2015/10/25/the-relationship-of-stress-hypermetabolism-to-essential-protein-needs/

https://pharmaceuticalintelligence.com/2015/10/24/the-relationship-of-s-amino-acids-to-marasmic-and-kwashiorkor-pem/

https://pharmaceuticalintelligence.com/2015/10/24/the-significant-burden-of-childhood-malnutrition-and-stunting/

https://pharmaceuticalintelligence.com/2015/04/14/protein-binding-protein-protein-interactions-therapeutic-implications-7-3/

https://pharmaceuticalintelligence.com/2015/03/07/transthyretin-and-the-stressful-condition/

https://pharmaceuticalintelligence.com/2015/02/13/neural-activity-regulating-endocrine-response/

https://pharmaceuticalintelligence.com/2015/01/31/proteomics/

https://pharmaceuticalintelligence.com/2015/01/17/proteins-an-evolutionary-record-of-diversity-and-adaptation/

https://pharmaceuticalintelligence.com/2014/11/01/summary-of-signaling-and-signaling-pathways/

https://pharmaceuticalintelligence.com/2014/10/31/complex-models-of-signaling-therapeutic-implications/

https://pharmaceuticalintelligence.com/2014/10/24/diabetes-mellitus/

https://pharmaceuticalintelligence.com/2014/10/16/metabolomics-summary-and-perspective/

https://pharmaceuticalintelligence.com/2014/10/14/metabolic-reactions-need-just-enough/

https://pharmaceuticalintelligence.com/2014/11/03/introduction-to-protein-synthesis-and-degradation/

https://pharmaceuticalintelligence.com/2015/09/25/proceedings-of-the-nyas/

https://pharmaceuticalintelligence.com/2014/10/31/complex-models-of-signaling-therapeutic-implications/

https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/

https://pharmaceuticalintelligence.com/2013/03/05/irf-1-deficiency-skews-the-differentiation-of-dendritic-cells/

https://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/

https://pharmaceuticalintelligence.com/2012/11/20/the-potential-for-nitric-oxide-donors-in-renal-function-disorders/

 

 

 

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Gene Expression and Adaptive Immune Resistance Mechanisms in Lymphoma

Larry H. Bernstein, MD, FCAP

 

Gene expression meta-analysis reveals immune response convergence on the IFNγ-STAT1-IRF1 axis and adaptive immune resistance mechanisms in lymphoma

Matthew A. Care1,2, David R. Westhead2 and Reuben M. Tooze1*
Care et al. Genome Medicine (2015) 7:96    http://dx.doi.org:/10.1186/s13073-015-0218-3

Background: Cancers adapt to immune-surveillance through evasion. Immune responses against carcinoma and melanoma converge on cytotoxic effectors and IFNγ-STAT1-IRF1 signalling. Local IFN-driven immune checkpoint expression can mediate feedback inhibition and adaptive immune resistance. Whether such coupled immune polarization and adaptive resistance is generalisable to lymphoid malignancies is incompletely defined. The host response in diffuse large B-cell lymphoma (DLBCL), the commonest aggressive lymphoid malignancy, provides an empirical model.

Methods: Using ten publicly available gene expression data sets encompassing 2030 cases we explore the nature of host response in DLBCL. Starting from the “cell of origin” paradigm for DLBCL classification, we use the consistency of differential expression to define polarized patterns of immune response genes in DLBCL, and derive a linear classifier of immune response gene expression. We validate and extend the results in an approach independent of “cell of origin” classification based on gene expression correlations across all data sets.

Results: T-cell and cytotoxic gene expression with polarization along the IFNγ-STAT1-IRF1 axis provides a defining feature of the immune response in DLBCL. This response is associated with improved outcome, particularly in the germinal centre B-cell subsets of DLBCL. Analysis of gene correlations across all data sets, independent of “cell of origin” class, demonstrates a consistent association with a hierarchy of immune-regulatory gene expression that places IDO1, LAG3 and FGL2 ahead of PD1-ligands CD274 and PDCD1LG2.

Conclusion: Immune responses in DLBCL converge onto the IFNγ-STAT1-IRF1 axis and link to diverse potential mediators of adaptive immune resistance identifying future therapeutic targets.

 

Emergence of clinically detectable malignant disease is associated with escape from tumour immune surveillance [1]. Two principal mechanisms may operate: on the one hand the immune systems loses the ability to detect the neoplastic population through changes in antigen presentation or editing of the antigen receptor repertoire; on the other hand initially effective immune responses may be rendered ineffective through development of an immune suppressive environment [2]. In the latter scenario, local expression of immune checkpoint components can be viewed as subversion of a physiological mechanism, which acts during chronic infections to balance effective immunity with immunemediated tissue damage [3].

In a range of cancers the density, location and functional polarization of tumour infiltrating lymphocytes are of prognostic value [4], providing evidence that the nature of immune evasion remains of importance after clinical detection. This is particularly relevant in the context of novel therapeutic strategies aimed at re-invigorating the “exhausted” anti-tumour immune response through immune checkpoint blockade [5, 6]. Gene expression analysis of bulk tumour tissue integrates expression profiles from multiple cellular sources, often allowing global assessment of the predominant vector of functional immune polarization. A paradigm has been proposed in which cancer-associated immune responses converge on a common “immunologic constant of rejection” characterized by a pattern of cytotoxic and T-cell immune responses and a dominant IFNγ-STAT1-IRF1 signalling axis [4, 7]. Linking the polarized pattern of interferon (IFN)γ-driven immune responses to the expression of immune checkpoints is the concept of “adaptive immune resistance” [5, 8]. In this model IFNγ signalling drives local feedback inhibition through the transcriptional regulation of ligands for the inhibitory receptor PD1 [5, 8]. The common association between cytotoxic responses and expression of IFN signatures and potential mediators of adaptive immune resistance has been further supported by analysis of solid tumour gene expression data from The Cancer Genome Atlas [9]. Importantly, such feedback may be mediated both at the immediate interface between tumour cell and cytotoxic lymphocyte, and by the establishment of a wider immune suppressive milieu in the tumour microenvironment.

The combination of convergent IFN-polarized immune responses [4, 7], coupled to IFN-driven adaptive immune resistance [5, 8], provides a powerful model with which to explain common pathologic associations in carcinoma and melanoma. The recent success of therapies targeting CTLA4 and PD1 immune checkpoints [10–12], combined with an extended range of other therapeutic options [6], means that evidence to support prioritization of therapeutic combinations in different tumour settings is required. Lymphoma, which comprises immune system malignancies, provides an instance in which these pathways are incompletely studied. Classical Hodgkin lymphoma is the archetype in which host response elements dominate to the point of obscuring the neoplastic B-cell clone [13], and in classical Hodgkin lymphoma PD1 pathway blockade has recently been described as a promising therapeutic approach [14]. Diffuse large B-cell lymphoma (DLBCL) is the commonest form of nodal lymphoma in the western world and represents an aggressive malignancy that frequently remains incurable. It is well established that this lymphoma type is associated with a varied extent of host response at diagnosis, which can include elements of IFN signalling [15]. Since several large data sets are publicly available [15–25], this malignancy represents an empirical human model in which to test the association between immune polarization and adaptive immune resistance mechanisms.

The “cell of origin” (COO) classification provides the dominant paradigm for our current understanding of DLBCL [24, 26]. This classification relates the gene expression profiles in DLBCL to those of germinal centre B cells (GCBs) or activated B cells (ABCs), the latter representing the initial stage of B-cell terminal differentiation to plasma cells. Although the COO classification allows the division of DLBCL based on expression of a restricted set of classifier genes into the two principal classes [24], a subset of cases show patterns of classifier gene expression that do not allow confident assignment to either GCB or ABC subsets. Such cases are referred to as “type 3” [24, 26], or “unclassified” [27, 28]. To avoid ambiguity we refer to these cases as COOunclassified DLBCL in the following. In a parallel “consensus cluster” classification developed by Monti et al. [15], it was shown that DLBCL could be divided into three categories characterized by preferential expression of genes linked to proliferation and B-cell receptor signalling, metabolic oxidative phosphorylation, or host response. The latter included multiple elements attributable to components of the immune system and supporting stromal cell types. It was noted that a greater proportion of COO-unclassified DLBCL belonged to the host/ immune response cluster, which had increased numbers of intra-tumoral T cells and macrophages and a relative decrease in neoplastic B cells [15]. We reasoned that the potential association of COOunclassified DLBCL with intense host responses provided a starting point for a meta-analysis of immune response elements in DLBCL. In originating from a prevailing paradigm this provided a wider biological and clinical context. Furthermore, by asking whether evidence supporting a common polarized immune response could be discovered from within the construct of the COO paradigm, we sought to avoid bias that might have arisen by focusing ab initio on components of the polarized immune response or immune checkpoints. With this approach we identify a distinct signature characterised by a pattern of cytotoxic T-cell and IFNγ-polarized immune response genes as a dominant pattern across ten DLBCL data sets encompassing 2030 cases. Using components of this polarized pattern we then explore the immune context of DLBCL in a fashion independent of COO class. We demonstrate the strong association with an IFNγSTAT1-IRF1 axis and an expression hierarchy of immune checkpoints/modulators, consistent with adaptive immune resistance as a common feature operating in DLBCL.
….

Normalisation and re-annotation of data For each data set the probes were re-annotated with the latest version of HUGO Gene Nomenclature Committee (HGNC)-approved symbols [30]. The complete HGNC list was downloaded (on 1 October 2014). Each probe was re-annotated to the latest approved symbol if an unambiguous mapping (i.e. single symbol mapping to approved symbol) could be determined, else the original gene name was maintained. Each data set was quantile normalised using the R Limma package [31]. The probes for each gene were merged by taking the median value for probe sets with a Pearson correlation ≥0.2 and the maximum value for those with a correlation <0.2 [15].

COO classifications We used the COO classifications assigned by the DLBCL automatic classifier (DAC) classifier in our previous work [32].

…..

Gene ontology analysis Meta-profile gene lists were assessed for gene ontology (GO) enrichment using the Cytoscape BiNGO tool [41]. GO and annotation files were downloaded from [42] (13 June 2014). The background reference was set to a nonredundant list of the genes present in the 11 data sets. The FDR rate (Benjamini and Hochberg) was set to ≤0.1.

Signature enrichment visualisation See Additional file 2 for an outline of the process for integrating and visualizing analysis of gene signature and ontology enrichments. The results from gene signature and gene ontology enrichment were used to create heatmap visualisations. For each meta-profile the top 100 most enriched signatures and 100 most enriched GO terms were used to construct a matrix of signatures against genes. This is a binary matrix with 1 s depicting an assigned signature/ GO annotation. Using Python a row-wise (gene correlation) and column-wise (signature correlation) phi coefficient was calculated. These were then hierarchical clustered using GENE-E [43] with complete linkage.

……

Results

Shared meta-profiles for COO-unclassified and COO-classified DLBCL Given the importance of the COO paradigm to both the biological and clinical assessment of DLBCL, we anchored our initial analysis on this classification. We previously developed a COO classifier implementation that allows the robust classification of multiple DLBCL data sets [32], which is currently in clinical usage in the context of a phase 3 clinical trial [44]. Applying this to the 11 largest publicly available DLBCL data sets (GSE10846 was split according to treatment into CHOP and R-CHOP components), encompassing 2030 cases [15–25], provided a resource for gene expression metaanalysis. To determine genes consistently linked to COO class we used both the consistency of differential expression between data sets as well as absolute level of differential expression to identify and rank genes associated with each class. We restricted the gene lists by applying a threshold of differential expression in 6 out of 11 data sets; we refer to these as meta-profiles. To explore the relationship of COO-unclassified DLBCL to each of the principal COO classes, we employed sequential pairwise comparisons (Additional file 1). From the initial comparison, we identified 127 genes associated with COO-unclassified DLBCL relative to both ABC- and GCB-DLBCL, while 209 genes were associated with both
COO classes relative to COO-unclassified DLBCL (Additional file 5; Fig. 1). The extent of overlap was highly significant (p=1.32E-157 and p=2.09E-200 for genes associated with COO-unclassified DLBCL or COO class, respectively). We subsequently refer to these sets of overlapping genes as COO-unclassified and COO-classified meta-profiles, respectively.

Figures not shown

Fig. 1 Consistent gene expression differences separate COO-unclassified DLBCL from either principal COO class. The overlap of genes consistently associated with either COO-classified DLBCL (left Venn diagram and Wordle) or COO-unclassified DLBC (right Venn diagram and Wordle) are shown. Left: the Venn diagram shows genes up-regulated in ABC (yellow) or GCB (blue) relative to COO-unclassified. Right: the Venn diagram shows genes up-regulated in COO-unclassified relative to ABC-DLBCL (brown) or GCB-DLBCL (turquoise). For the Wordles, word size is given by differential expression (between contrasts) to the power of median-fold change
COO-unclassified DLBCL is enriched for features of a polarized immune response To assess underlying biology in the COO-classified and COO-unclassified meta-profiles we developed an approach for integrated analysis of GO and gene signature enrichment (Additional file 2) which applies hierarchical clustering to reciprocally assess the relationships of enriched ontology and signature terms and associated genes contributing to enrichments (Additional file 6). The results are displayed as heatmaps of the hierarchically clustered correlations. In the COO-classified meta-profile a striking representation of genes linked to cell proliferation resulted in multiple distinct clusters of enriched terms reflecting a wide range of processes associated with cell proliferation (Fig. 2a; Additional file 7). In addition to this, distinct enrichment of signatures of the B-cell lineage was evident. From the gene perspective this was reflected in one main branch associated with cell cycle and cell proliferation, and the second including two principal subclusters associated on the one hand with RNA binding and processing, and on the other with core B-cellassociated genes (Fig. 2b; Additional file 8).

Fig. 2 Integrated gene signature and ontology enrichment analysis demonstrates association of the COO-classified meta-profile with cell proliferation and B-cell signatures. a The top gene signature and ontology terms enriched in the COO-classified meta-profile, clustered according to the correlation of signatures given their gene membership. b The corresponding clustering of genes contributing to signature and ontology term enrichments for the COO-classified meta-profile, clustered according to correlation of genes given their signature membership. To the right general categories corresponding to major correlation clusters are illustrated. Corresponding high resolution versions are available in Additional files 7 and 8

In contrast the COO-unclassified meta-profile was linked to terms related to T-cell populations, T-cell receptor signalling and T-cell activation. While the second principal branch of ontology/signature terms was linked to additional more diverse immune response elements (Fig. 3a; Additional file 9). Hierarchical clustering from the gene perspective (Fig. 3b; Additional file 10) generated a principal branch related to T cells composed of a cluster of genes representing core elements of the T-cell state (CD2, CD3D, CD3E, CD3G, CD28 and TRBC1) and another cluster of genes with T-cell associations, including BCL11B, GZMA, GZMK, MAF and STAT4. The second principal branch of the hierarchical tree included genes derived from monocytes and other immune/host response signatures. This also included a subcluster comprising IFNG, and interferon responsive genes GBP1 and IFITM1, as well as the chemokine receptors CCR5, CXCR3 and CXCR6, which are linked to Th1 polarized T-cell populations [45, 46]. We therefore conclude that COO-unclassified DLBCL is generally distinguished from COO-classified DLBCL by a predominant T-cell immune response with skewing toward IFNG gene expression. Furthermore the paucity of both proliferation and B-cell gene expression is indicative of a relatively low representation of neoplastic B cells.

Fig. 3 Integrated gene signature and ontology enrichment analysis demonstrates association of the COO-unclassified meta-profile with polarized immune response. a The top gene signature and ontology terms enriched in the COO-unclassified meta-profile, clustered according to the correlation of signatures given their gene membership. b The corresponding clustering of genes contributing to signature and ontology term enrichments for the COO-unclassified meta-profile, clustered according to correlation of genes given their signature membership. To the right general terms corresponding to major correlation clusters are illustrated (NOS not otherwise specified). Corresponding high resolution versions are available in Additional files 9 and 10

A cytotoxic and interferon polarized immune response as an independent molecular feature of DLBCL We next addressed to what extent the identified polarized pattern of immune response was selective for COO-unclassified DLBCL or whether equivalently intense expression of polarized immune response genes might be detectable amongst some DLBCL cases that could be assigned to a principal COO class. As noted above, the COO-unclassified meta-profile separated on hierarchical clustering from the gene perspective into two branches, one of which was more strongly linked to core T-cell and cytotoxic genes (Fig. 4). To examine the relative ranking of genes belonging to these two hierarchical clustering branches within the COOunclassified meta-profile we superimposed the cluster membership onto scatter plots of differential expression ranking. We first ranked and then plotted genes
belonging to the meta-profile by median fold differential expression in the comparison of COO-unclassified with ABC- or GCB-DLBCL. This demonstrated a significant overall correlation in the differential expression of COO-unclassified meta-profile genes relative to either principal COO class. Furthermore, genes belonging to the “T-cell cluster” (cluster 1) were significantly skewed toward most consistent association with COO-unclassified DLBCL (Additional file 11). To address whether the consistency of differential detection between data sets would alter this conclusion we ranked genes by a measure derived from both the number of data sets (consistency of differential expression) in which a gene was differentially expressed and the normalised median fold differential expression (Additional file 12). This again showed a significant overall correlation and a skewing of the T-cell cluster toward most consistent association with COO unclassified DLBCL (p=6.57E-06, hypergeometric test; Fig. 4). However, using either approach IFNG was identified as amongst the cluster 2 genes most consistently linked to COO-unclassified DLBCL.

Fig. 4 Genes most consistently associated with COO-unclassified DLBCL are related to a polarized immune response. The two principal branches of the gene-centred hierarchical clustering tree of the COO-unclassified meta-profile are illustrated on the left. Colour-coding identifies: red cluster 1, corresponding to the T-cell cluster; black cluster 2, IFN and monocyte/immune NOS (not otherwise specified). On the right the relative rank of differentially expressed genes contributing to the COO-unclassified meta-profile is plotted using a differential expression ranking, derived from the number of data sets with differential expression to the power of normalized median fold change; the x-axis indicates differential expression rank in the comparison COO-unclassified versus ABC-DLBCL; the y-axis indicates differential expression rank in the comparison COO-unclassified versus GCB-DLBCL. Cluster membership is superimposed on the scatter plot of differential expression rank according to the colour coding shown (red cluster 1, black cluster 2). The 16 genes most consistently separating COO-unclassified DLBCL from either ABC- or GCB-DLBCL are illustrated below with cluster membership and mean differential expression rank shown. See corresponding Additional file 11

To examine the contribution of polarized immune response genes associated with COO-unclassified DLBCL across all data sets on a case-by-case basis we developed a linear additive classifier. For this we employed the 16 genes most strongly linked to COO-unclassified DLBCL derived from analysis using both the consistency/data set number and median fold differential expression. Given the contribution of core T-cell elements, cytotoxic genes and IFNG, we consider this to represent an integrated assessment of a polarized immune response. We ranked all cases in each data set by this linear score and plotted the incidence of cases classified as ABC, GCB and unclassified on this ranking. Overall, individual COO-unclassified DLBCL cases showed a stronger association with the polarized immune response score relative to either ABC- or GCB-DLBCL (Fig. 5a; Additional file 13). This was particularly evident in the larger data sets GSE31312, GSE22470 and GSE10846. However, ABC- and GCBDLBCL cases with high levels of expression of the polarized immune response score were present in all data sets.

Fig. 5 The polarized immune response is a dominant feature across DLBCL, independent of COO class. a The incidences of individual cases across all data sets (note GSE10846 is subdivided into CHOP and R-CHOP treated components) ranked according to polarized immune response score. The top and bottom 25 cases for each data set are illustrated with colour coding for COO class shown in the top bar (yellow ABC, blue GCB, green unclassified), class confidence assigned during classification shown in the middle bar (blue low confidence to red high confidence), and polarized immune response score shown in the bottom bar (blue low polarized immune response score to red high polarized immune response score). b Complete results for data sets GSE10846 R-CHOP and GSE31312, showing all cases ranked by polarized immune response score. Each heatmap displays class assignment, classification confidence and polarized immune response score summary as in (a) followed by COO-classifier gene expression (yellow and blue bars), the 16 genes of the polarized immune response score (green bar), and the extended set of COO-unclassified meta-profile genes (black bar). A corresponding high-resolution figure comprising equivalent representation for all data sets is provided in Additional file 13

To assess whether the 16-gene score also reflected the expression of other genes associated with the immune response in COO-unclassified DLBCL we added further components of the meta-profile. Expression of these genes followed the overall pattern of expression of the 16-gene score across all DLBCL data sets (Fig. 5b; Additional file 13). Thus, the 16-gene score provides a tool with which to identify the overall pattern of this polarized immune response in DLBCL. Since some COO-unclassified DLBCL cases in all data sets showed low polarized immune response scores, we examined the pattern of T-cell gene expression further by hierarchical clustering within each COO class. This demonstrated, particularly in the larger data sets such as GSE31312 and GSE22470, that COO-unclassified DLBCL could be segregated into principal groups with a subset of cases characterized both by weak expression of COO-classifier genes and weak expression of polarized immune response genes (Fig. 6; Additional file 14). Within the ABC- and GCB-DLBCL subsets there was a common concordance between expression of core T-cell genes and components of the polarized immune response. Only a few cases, particularly in the GCBDLBCL subset, could be identified in which core T-cell genes were co-expressed in the absence of other elements of the polarized response. These cases were, however, too few to allow meaningful analysis (data not shown). Thus, across all DLBCL data sets the expression of core T-cell genes is paralleled by the expression of genes linked to functional polarization irrespective of COO class.

Fig. 6 The polarized immune response subdivides COO-unclassified DLBCL and identifies subsets of cases within ABC- and GCB-DLBCL classes. Heatmaps illustrate data for GSE10846 R-CHOP and GSE31312 hierarchically clustered according to all genes shown, and constrained by COO class assignment. Assigned COO class is shown above each heat map by the blue (GCB), green (COO-unclassified) and yellow (ABC) bars. To the right is shown the corresponding general category of genes: yellow ABC-classifier genes, blue GCB-classifier genes, green polarized immune response score genes, and black extended COO-unclassified meta-profile. A corresponding high-resolution figure comprising equivalent representation for all data sets is provided in Additional file 14

Polarised immune response and COO-unclassified DLBCL do not overlap significantly with signatures of primary mediastinal B-cell lymphoma COO-unclassified DLBCL cases lacking both polarized immune response and COO-classifier gene expression are distinct from the subset of cases in which the extent of the polarized immune response obscures the characterization of the neoplastic B-cell population. At least two principal explanations could be considered for this subgroup: on the one hand these might include cases in which gene expression was technically challenging with poor representation of tumour cell RNA; alternatively, they might include a subset of large B-cell lymphoma which fails to express COO-classifier genes at significant levels. Primary mediastinal B-cell lymphoma (PMBL) is a biologically distinct subgroup of large B-cell lymphoma, more common in women, with a mediastinal localization, distinct molecular genetics and possible derivation from a thymic B-cell population [47]. This lymphoma class can be associated with a pattern of gene expression distinct from either GCB- or ABC-DLBCL. While many PMBL cases would be excluded on the basis of diagnosis from conventional DLBCL gene expression data sets, it was possible that some PMBL cases might contribute to the COO-unclassified DLBCL cases, in particular those lacking a polarized immune response signature. To address this we used the 23-gene PMBL signature described by Rosenwald et al. [40], and first tested for enrichment within the COO-classified and COO-unclassified meta-profiles, but this showed no evidence of significant enrichment, nor was a signature separating PMBL from Hodgkin lymphoma enriched (Additional file 6). We next used the 23-gene PMBL signature in place of the extended immune response gene list to reanalyse the DLBCL data sets by hierarchical clustering (Additional file 15). We found no evidence of distinct clusters of cases identifiable with the 23-gene PMBL signature amongst COO-unclassified DLBCL, although a few elements of the 23-gene signature, most notably PDCD1LG2, CD274 and BATF3, do correlate with the polarized immune response. In contrast, in several data sets small clusters of cases were identifiable with coordinated high expression of the 23 genes of the PMBL signature, but such cases were classifiable as GCB-DLBCL, suggesting a greater overlap of PMBL signature gene expression amongst cases otherwise classifiable as GCB-DLBCL rather than ABC-DLCBL or COO-unclassified DLBCL. Thus, we found no gene expression-based evidence for a significant contribution of PMBL-like gene expression patterns amongst COOunclassified DLBCL in the data sets analysed. Inclusion of PMBL-like cases does not have a major impact on the detection of the polarized immune response signature, nor provide an explanation for the subset of COOunclassified DLBCL that lacks both COO-classifier and polarized immune response gene expression.

A polarized immune response is associated with improved outcome in DLBCL Across several cancer types the extent of tumour infiltrating lymphocytes, and their polarization toward cytotoxic T/natural killer (NK) cell gene expression linked to an IFNγ-STAT1-IRF1 signalling axis has been identified as a feature associated with good prognosis [4]. We therefore asked whether the expression of the polarized immune response signature, alone or taken in conjunction with COO class, was associated with differences in overall survival. Currently DLBCL is treated with an immunochemotherapy regimen, R-CHOP, which combines the anti-CD20 therapeutic monoclonal antibody rituximab with cyclophosphamide, hydroxydaunorubicin, vincristine (Oncovin), and prednisolone. Based on the success of the R-CHOP regimen, current treatment and future therapeutic trials in DLBCL will be based on immunochemotherapeutic approaches encompassing rituximab or related therapeutic antibodies. Therefore, only those data sets (GSE10846, GSE31312 and GSE32918) encompassing R-CHOP-treated cases associated with appropriate survival data were considered. This analysis demonstrated a consistent trend toward a reduced hazard ratio of death with increasing polarized immune response score across all three R-CHOP-treated DLBCL data sets. This reached statistical significance when considered independently of COO class in data sets GSE32918 and GSE31312, the latter representing the largest data set of R-CHOP-treated DLBCL [23]. However, in these two data sets the polarized immune response score was also significantly associated with lower age. When considered according to COO classification a consistent trend toward better outcome with high polarized immune response score was observed across all three categories. This trend was most pronounced for GCB-DLBCL, and reached statistical significance for improved outcome associated with high polarized immune response score in the largest data set GSE31312 (Additional file 16; Fig. 7). We conclude, therefore, that the presence of a polarized and IFNγ-associated immune response shows an association with good outcome which is modified by consideration of COO class, such that in the context of current R-CHOP therapy a polarized immune response is most consistently linked to improved outcome in patients with GCB-DLBCL.

Fig. 7 A high polarized immune response score is associated with improved outcome in R-CHOP-treated GCB-DLBCL. The figure illustrates Kaplan– Meier plots of overall survival derived from R-CHOP-treated DLBCL cases from data sets GSE10846, GSE31312 and GSE32918. Illustrated is the overall survival for the top and bottom 25 % of cases divided by polarized immune response score. The left graphs illustrate results independent of COO class and the right graphs results for cases assigned to the GCB-DLBCL class. CI confidence interval, HR hazards ratio

Polarization along an IFNγ-STAT1-IRF1 axis is a defining feature of the DLBCL immune response While the above analysis pointed to a common convergence onto a cytotoxic and IFNγ-polarized immune response in DLBCL, not all components of the IFNγSTAT1-IRF1 axis were sufficiently differentially expressed between COO-classified and COO-unclassified DLBCL to be identified by this approach. In order to explore the
DLBCL-associated immune response in a fashion which was not constrained by the COO paradigm we reanalyzed the DLBCL data sets, assessing the consistency and degree of correlated gene expression across all data sets relative to a selected “focus gene” (Fig. 8a; Additional files 3 and 4). We followed this by applying the integrated signature and GO enrichment analysis (Additional file 17). As focus genes we selected two components of the 16gene polarized immune response signature, TRAT1 and FGL2, to reflect origin from the two branches of the COO-unclassified meta-profile (Fig. 8b; Additional files 18 and 19). TRAT1 was selected as the most highly correlated gene from cluster 1 (Fig. 4), while FGL2 was selected as the second most highly correlated gene in cluster 2, and of more established immunologic interest than TC2N and less overt connection to immune response polarization than IFNG, the other two genes derived from cluster 2 that contribute to the 16-gene polarized immune response classifier. Genes correlating with TRAT1 could be assigned to clusters of signatures and GO terms related to T-cell state, and T-cell signal transduction, cell motility and interferon response. For FGL2 as the focus gene a similar pattern emerged, including an expanded cluster of signature enrichments related to interferon responses, including some derived from models of viral infection, and an additional association with monocyte/macrophage-derived signatures. To examine the strength of correlation with IFN responsive genes we ranked genes by median correlation, plotted rank against median gene correlation for each focus gene context and assessed the distribution of selected IFN signature genes (derived from the previous analysis) on the resulting correlation curves. We applied this approach using TRAT1 and FGL2 as focus genes, but observed similar results with all 16 genes of the polarised immune response classifier (Fig. 9; Additional files 20). In either context IFN pathway genes were consistently present within the leading edge of most correlated genes, including IFNG, STAT1, IRF1, GBP1, GBP5 and IDO1. These genes were also consistently present within the leading edge when considering a more generic T/NK cell-associated gene, CD2, as focus gene. Components of the IFNγ-STAT1-IRF1 axis therefore emerge as a consistent and dominant feature of the DLBCL immune environment linked to expression of a wider complement of IFN-responsive genes.

Fig. 8 A focus gene analysis independent of COO class verifies the dominant polarized immune response in DLBCL. a An outline of the focus gene analysis (high resolution version in Additional file 3). Upper panel: the approach within each data set, with initial selection of the 80 % most variable genes, and subsequent generation of linked matrices of gene correlation values and associated p values. Middle panel: the merging of all data sets (11 data sets; data set GSE10846 subdivided by treatment type) is shown to give matrices of median correlations and p values. Lower panel: the selection of an individual focus gene for downstream analysis. b Results of integrated gene signature and ontology analysis for two focus genes (left panel TRAT1) and (right panel FGL2) displaying the clustering of enriched signature and GO terms. General terms corresponding to major correlation clusters are illustrated to the right of each heatmap. Corresponding high resolution versions are available in Additional files 18 and 19, which also include the corresponding heatmaps clustered from the gene perspective

Fig. 9 IFN-responsive genes and the IFNγ-STAT1-IRF1 axis are amongst the leading edge of highly correlated DLBCL immune response genes. Correlation curves were generated from the focus gene analysis by ranking genes according to median correlation, and then plotting the gene correlation rank (x-axis) against the corresponding median gene correlation (y-axis, median Rho). This illustrates both the relative strength of correlations for each focus gene and identifies a leading edge of genes with most significant correlations. The position of a set of IFN-associated genes was plotted for each focus gene context as indicated in the figure. Note only the top 2000 of 20,121 genes tested are illustrated. See corresponding Additional file 20
IFNγ-STAT1-IRF1 axis and adaptive immune regulatory pathways in DLBCL In the model of adaptive immune resistance IFNγ-driven expression of PD1 ligands CD274 and PDCD1LG2 on tumour cells and the microenvironment limits local T-cell responses [5, 8]. We reasoned that the hierarchy of gene expression correlations would allow a ranking of immune checkpoint/modulatory gene expression linked to the IFNγ-STAT1-IRF1 polarized response in DLBCL. In this pathway STAT1 and IRF1 encode the transcriptional regulators; we therefore selected these along with CD2 as a generic representative of the T/NK cell response for analysis (Fig. 10; Additional file 4). When considering immune modulatory/checkpoint genes a consistent cluster of three genes, LAG3, IDO1 and FGL2, emerged as most highly ranked and amongst the leading edge in all three focus gene contexts. In contrast, CD274 and PDCD1LG2 showed significantly weaker correlations with each focus gene, but nonetheless remained well correlated in comparison with all genes tested (rank <1000 out of 20,121 tested). To further confirm this pattern we extended the analysis to all 16 genes of the polarized immune response classifier, and observed similar patterns of gene correlation ranking (Additional file 21). Since the relative contribution of immune modulatory/checkpoint gene expression in tumour cells themselves relative to the wider microenvironment cannot be determined from these analyses, we conclude that, in addition to CD274 and PDCD1LG2,a wider complement of immune modulators provides a potentially high degree of redundancy in adaptive immune resistance in DLBCL. Amongst these components IDO1, FGL2 and LAG3 are particularly strongly correlated with IFNγ polarized immune responses.

Discussion

The common convergence of cancer immune responses onto patterns of cytotoxic and IFNγ-dominated pathways has been summarised in the concept of an “immune constant of rejection” [4, 7]. In parallel the model of adaptive immune resistance argues for the control of such immune responses via local feedback driven through IFN-mediated upregulation of immune checkpoints [5, 8]. Our analysis here provides extensive empirical evidence across currently available large DLBCL data sets that this combination of IFNγ polarisation and induction of adaptive immune resistance mechanisms is a feature of the immune response to DLBCL. Unbiased analysis of gene expression correlations moreover suggests a hierarchy of IFN-associated immune modulatory gene expression with LAG3, IDO1 and FGL2 as key elements in this context. Thus, adaptive immune resistance is likely to provide an important component of immune evasion in DLBCL.

Other mechanisms of immune evasion have been previously identified as playing an important role in the pathogenesis of DLBCL, including mutation and deletion of B2M and CD58, and amplification of genomic regions encompassing genes encoding PD1 ligands [48, 49]. Furthermore previous studies have demonstrated the presence of PD1 expression on infiltrating T-cell populations and PD-L1(CD274) on tumour cells and in the microenvironment of DLBCL and related neoplasms [50, 51]. In the context of gene expression profiling, morphologically defined T-cell and histiocyte-rich large B-cell lymphoma, which represents a relatively rare subcategory, has been characterized by evidence of an IFNassociated immune response, linked on the one hand with over-expression of PD1 (PDCD1) on infiltrating T cells when compared with classical Hodgkin lymphoma [52], or the expression of IDO1 when compared with nodular lymphocyte predominant Hodgkin lymphoma, another relatively rare lymphoma subtype [53]. Indeed, expression of IDO1 has been defined as a feature associated with poor outcome in generic DLBCL in one patient series [54]. Thus, the involvement of several pathways of immune modulation in large B-cell lymphomas is supported by prior studies.

Using the 16-gene polarized immune response score we have ranked DLBCL cases across multiple data sets, and demonstrate that a substantial fraction of cases regardless of COO class are linked to a polarized immune response. In the context of the COO classification, the dominance of this immune response at the expense of proliferating B cells provides the most common explanation for DLBCL cases that are “unclassifiable” as originally suggested by Monti et al. [15]. Equally important is the identification of a distinct group of DLBCL characterized by an absence of host response elements, which is consistent with “immunological ignorance”,  a feature which in other cancers is associated with poor response to immune checkpoint blockade [12]. These cases are also consistent with a model of host tissue “effacement” proposed by Scott and Gascoyne [49] as distinguishing subsets of aggressive lymphomas. Immune evasion in DLBCLs can be associated with loss of MHC class I expression consequent on mutation and/or deletion of B2M, which may be further accompanied by inactivation of CD58 [48], and a prediction might be that such cases would be enriched in the subset characterized by apparent immunological ignorance. However, analogous lesions affecting B2M were recently identified as recurrent events positively associated with cytotoxic gene signatures in solid tumours [9]. This suggests a model in which adaptive immune resistance mechanisms may be followed by somatic genetic alterations that further enhance tumour immune escape. Whether a similar positive association between cytotoxic response and B2M or CD58 mutation status exists in DLBCL is, to our knowledge, not established.

Across several cancer types the intensity of tumour infiltrating lymphocytes and their functional polarization has proved to be of prognostic significance in the absence of specific immune checkpoint blockade [4, 55–57]. Our analysis indicates that a trend toward an improved outcome in association with a more intense polarized immune response is generally maintained in the context of DLBCL treated with the current immunochemotherapy regimen, R-CHOP. However, this benefit is not equivalent across all DLBCL when considered in relation to COO class, and is most pronounced for GCB-DLBCL. Indeed, in the largest available data set of R-CHOP-treated DLBCL, GSE31312 [23], a substantial group of patients with both a GCB-DLBCL classification and a high polarized immune response score appeared curable with current therapy. As a statistically significant association is not consistently observed across all three data sets of DLBCL treated with R-CHOP, and there is a potentially confounding association with young age, the overall prognostic value of this association remains uncertain in the context of current therapy. Additional features of the host response, which did not emerge as principal discriminants between COO-classified versus COO-unclassified DLBCL, such as contributions from macrophage/monocyte lineage cells, may add value to immune response classifiers. These will need to be considered alongside the polarized immune response signature in future work. Nonetheless, the analysis presented here demonstrates a graded pattern of immune response in DLBCL, with one extreme characterized by minimal cytotoxic immune response signature and tendency to poor outcome, and another extreme characterized by intense polarized immune response and a tendency toward better outcome which is modified by COO class. In other settings the pattern of pre-existing immune response prior to immune checkpoint therapy has proved to be of predictive value [11, 12, 58, 59]. Based on this evidence, it is the subset of DLBCL cases with preexisting polarized immune response which is most likely to benefit from immune checkpoint/modulatory therapy, and is readily identifiable in a quantitative fashion from gene expression data.

Immune checkpoint inhibitors are already under evaluation in the context of large cell lymphomas [60, 61]. Recent clinical trials with PD1 pathway blockade have shown substantial promise in Hodgkin lymphoma [14], as in other tumour types [11, 12, 62]. Combining immune checkpoint inhibitors may hold particular promise, and both LAG3 and IDO1 are therapeutic targets with novel agents in current clinical evaluation. Our analyses support these as high priority candidates for therapeutic evaluation in DLBCL alongside PD1 blockade. In addition to direct interventions specifically targeting immune checkpoints, signalling pathways that mediate survival of neoplastic B cells, and are the targets of novel therapeutic agents in lymphoma, overlap with pathways controlling T-cell responses. Such agents have the potential to de-repress cytotoxic T-cell populations and promote anti-tumour immunity [63]. Thus, companion biomarkers evaluating the potential association between pre-existing immune response at diagnosis and treatment response should arguably also be included in the setting of lymphoma clinical trials where agents targeting lymphocyte signalling pathways are being evaluated.

A notable element of the DLBCL immune response is the consistent association with FGL2 expression. This encodes fibrinogen-like 2 prothrombinase, a protein that has dual roles as a pro-coagulant and immune modulator. FGL2 has been shown to act as an immune responsive coagulant in settings such as foetal loss driven by Th1 polarized immune responses [64] and fulminant hepatitis [65]. Subsequently, FGL2 has been implicated as a repressor of T-cell activation both in the ability of recombinant FGL2 to block graft rejection [66] and in the context of Fgl2 knockout mice developing autoimmune glomerulonephritis [67]. In several experimental models FGL2 has been associated with suppression of cytotoxic and Th1-polarized immune responses [67–69]. FGL2 effects in DLBCL could relate to both pro-coagulant and immune modulatory functions. In DLBCL FGL2 expression correlates with multiple elements of the IFNγSTAT1-IRF1 axis; supporting direct regulation, FGL2 expression has previously been shown to be responsive to IFNγ in Tcells [70, 71], and was shown to act downstream of IRF1 in Th1-driven foetal loss [64]. Thus, the relationships in DLBCL suggest that FGL2 may provide an additional element of negative feedback and adaptive immune resistance, which is potentially suitable for therapeutic targeting [72, 73].

We note that some DLBCL cases with a prominent immune response may be associated with Epstein-Barr virus (EBV) infection and oncogenic drive. In the metaanalysis approach taken here the contribution of EBV cannot be systematically assessed from available data since EBV status is incompletely annotated, and not necessarily assessed using both immunohistochemistry for EBV LMP1 and RNA-FISH for EBERs. Immune surveillance is known to contribute to the control of EBVmediated tumours [74], and the presence of high EBV loads can contribute to the establishment of an exhausted cytotoxic response [75]. Indeed, there are significant overlaps between the gene expression profiles of the immune response in EBV-associated large cell lymphomas occurring in the post-transplant setting [76] and the polarized IFNγ-associated gene expression that is evident from our DLBCL meta-analysis. However, while the frequency of EBV infection in generically diagnosed DLBCL has been established at close to 10 % [77], significant expression of genes linked to the polarized immune response is more frequent across DLBCL data sets. An overlap of gene expression profiles between the immune response targeting EBV-driven and EBV-independent lymphomas is consistent with the model of convergent patterns of “immune rejection” across diverse immune contexts [4, 7]. It is arguable that the principal predictive factor of response to immune checkpoint inhibition will be the presence of a pre-existing polarized immune response and the mechanisms controlling its chronic activation/exhaustion rather than the nature of the initial triggering antigens whether viral or cancer-associated.
Conclusions The analysis presented here supports the central importance of convergent patterns of immune response linked to the IFNγ-STAT1-IRF1 axis, coupled to IFN-driven feedback pathways in DLBCL. This argues for the generalisable nature of these interconnected mechanisms, and implicates a hierarchy of immune modulators, known to promote the establishment of an immunosuppressive microenvironment [2], in the process of IFNγ-driven adaptive immune resistance.

Additional files
Additional file 1: Figure S1. Outline of meta-profile generation using COO classification. Upper panel: the data sets used. Please note one data set, GSE10846, is divided into two component parts reflecting underlying differences in treatment (CHOP versus R-CHOP), giving a total of 11 separate data set components. Illustrated are all cases for each data set, subdivided by COO classification established using the DAC classifier [32] and ranked by classification confidence, with classifier genes illustrated on the right (yellow bars ABC classifier genes and cases, blue bars GCB classifier genes and cases, green bars COO-unclassified cases). Middle panels: the pairwise comparisons of differentially expressed genes between three classes, and the integration of differentially expressed genes across data sets. Bottom panels: the resulting meta-profiles of differentially expressed genes for both components of the three possible pairwise comparisons are shown as Wordles for illustrative purposes (complete details provided in Additional file 5). (PDF 2024 kb)

Additional file 2: Figure S2. Outline of the process for integrating and visualizing analysis of gene signature and ontology enrichments. The flow diagram illustrates the process for integrating gene signature and ontology enrichments. The initial assessment of overlap between meta-profiles derived from the comparison of ABC-DLBCL versus COO-unclassified (CU) DLBCL and GCB-DLBCL versus COO-unclassified (CU) DLBCL is shown at the top of the figure, followed by the parallel analysis of gene ontology (BiNGO) and hypergeometric testing of signature enrichments. Next a matrix is illustrated showing the occurrence of genes versus enriched signatures (green fill), followed by analysis of correlations (Phi coefficient) by column (signature/ontology terms) or by row (genes) and hierarchical clustering. (PDF 1658 kb)

Additional file 3: Figure S11. An outline of the focus gene approach, and a high resolution image to accompany Fig. 8a. Upper panel: the approach within each data set with initial selection of the 80 % most variable genes, and subsequent generation of linked matrices of gene correlation values and associated p values. Middle panel: merging of all data sets (11 data sets; data set GSE10846 subdivided by treatment type) to give gene by gene matrices of median correlations and p values. Lower panel: the selection of an individual focus gene for downstream analysis. (PDF 1375 kb)

Additional file 4: Table S5. Lists of correlated genes for selected focus genes. (XLSX 16708 kb)

Additional file 5: Table S1. Lists of Meta-profile genes differentially expressed between DLBCL COO classes. (XLSX 2318 kb)

Additional file 6: Table S2. Lists of enriched gene signature and gene ontology terms for COO-classified and COO-unclassified meta-profiles. (XLSX 2848 kb)

Additional file 7: Figure S3. High resolution image corresponding to Fig. 2a. Integrated gene signature and ontology enrichments for COOclassified meta-profile clusters from signature and ontology term perspectives. The figure represents the hierarchical clustering of enriched gene signature and ontology terms related to the COO-classified meta-profile. Correlations are illustrated in heat maps on a blue (least) to red (most) scale as indicated at the top of the figure. Along the edges of the heatmap the signature terms are provided (and correspond to terms listed in Additional file 6). The FDRcorrected p value for enrichment of the signature is illustrated as a bar on either side of the heatmap, along with an indication of the type of term (signature versus ontology) and the origin of the terms as indicated in the figure. (PDF 283 kb)

………   Additional file 21: Figure S15.

 

Fig. 10 Immune-modulatory and checkpoint gene expression is strongly correlated with elements of the IFNγ-STAT1-IRF1 axis in DLBCL. IRF1 and STAT1 along with CD2 were analysed as focus genes, and resultant correlation curves are illustrated. Genes were plotted according to correlation rank (x-axis) against median gene correlation (y-axis, median Rho). The position of immune checkpoint/modulatory genes on the resulting curves was plotted for each focus gene as indicated in the figure. Note only the top 2000 of 20,121 genes tested are illustrated. See corresponding Additional file 21

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