PATTERN RECOGNITION TUMOR TARGETING*

Transcription

1 Transactions of the Integrated Biomedical Informatics & Enabling Technologies Symposium Journal 2004, 1; Windber Research Institute, Windber PA, USA. PATTERN RECOGNITION TUMOR TARGETING* Arnold Glazier Drug Innovation and Design, Inc., 9 Brandeis Road, Newton, Ma , USA. aglazier@diadinc.com The author has a financial interest in Drug Innovation and Design, Inc., which is developing pattern recognition targeting technologies. Received December 1, 2003, Accepted January 27, 2004 Tumor cell evolution is an astronomically diverse, unpredictable, indeterminate, stochastic process and is the major barrier to the effective treatment of metastatic cancer. A new conceptual model is needed that can address the evolutionary nature of cancer and provide a pathway to the specific cure or control of metastatic disease. The foundation of such a model must rest on the information requirements for the specific detection and destruction of the set of all tumor cells that are probable to evolve in the patient. An analysis of these information requirements is presented. We conclude that to specifically identify and destroy the set of all tumor cells that are probable to evolve requires the ability to detect and target abnormal patterns of normal cellular machinery that enable or reflect malignant behavior, i.e., proliferation and invasiveness in an abnormal context. Approximately 5 to 10 independent protein patterns must be simultaneously targeted. Many simple, tumor-specific patterns of normal proteins are currently known. Practical chemical approaches are proposed for the development of drugs that will kill cells if and only if the cells express the targeted pattern. One method exploits the large difference in functional binding affinity between mono-valent and multi-valent binding. We believe that the development of pattern recognition targeting technologies must become a major priority and will be required for the consistent and specific cure or control of metastatic cancer. Key Words: Pattern recognition, tumor targeting, tumor evolution Introduction Despite heroic efforts, it has not been possible to extend the successes of multiple drug combination chemotherapy in the cure of childhood leukemia, lymphoma, and testicular cancer to metastatic cancer in general. 1,2 The overall five-year survival rate for all cancers combined remains approximately 60%. In this paper we critically examine what is required to break this impasse and to specifically cure or control metastatic cancer. Essential requirements for the consistent, specific, and comprehensive detection and destruction of an evolutionary tumor cell population are described. We conclude that the specific cure or control of metastatic cancer is a solvable engineering problem, within the context of these requirements. Cancer is an evolutionary disease. A vast amount of experimental data corroborates the theory that cancer cell populations are characterized by random genetic and epi-genetic alterations and natural selection, in accord with Darwin s Theory of Evolution. 3,4,5,6,7 Advances in DNA sequencing and chromosomal analysis have exposed to clear view the vast genetic diversity of tumor cell populations. Natural selection has been repeatedly observed in tumor cell populations exposed to selective pressures. Tumor cell evolution is stochastic and unpredictable. Stochastic processes and events trigger genetic damage and create random genetic diversity. Although the mechanisms may be known, the exact events that cause genetic alterations are unpredictable, and indeterminate. For example, consider, the impossibility of predicting the exact genetic lesions induced by radon *Published online at ISSN: /MS

2 during the evolution of lung cancer. Natural selection is also fundamentally stochastic. The selective pressures that determine tumor cell viability and survival are contingent upon both the internal properties of the cell and the entire environment that interacts significantly with the cell. Cell survival involves multiple, complex, interacting, stochastic processes and factors including: the genetic constitution of the cell and of all other cells in competition for survival; the location of the cell; the immune response; drugs; nutrients; hormones; O 2; etc. In addition, tumor cell evolution is iterative. Outputs from one generation become inputs for the next generation and further compound the chaos. The potential complexity is almost unlimited. Cancer cells can have thousands of random mutations per cell. 8,9 Ten thousand point mutations randomly distributed among the 3 billion base pairs of the human genome, can generate approximately 10 65,000 different genotypes. (If the point mutation can correspond to a deletion or change to one of three new bases, then the number of permutations is approximately [( ) 10, ,000 ] 10,000!) This estimate includes mutations to noncoding DNA. Such mutations potentially can have significant biological effects. 10 However, this number does not even include complex chromosomal rearrangements, deletions, duplications, aneuploidly, or epigenetic modifications, all of which can have profound biological effects. Most mutations are of no clinical significance. However, enough genetic alterations are significant to cause: loss of initiating oncogenes; new mechanisms of malignancy; contradictory properties; escape from immune attack; drug resistance; and treatment failures. 11,12,13 Tumor cell evolution fundamentally limits what can be known about cancer. There are compelling experimental data that specific genetic alterations, specific oncogenes, and the dysregulation of specific metabolic pathways can have etiologic and pathologic significance in cancer. 14,15 However, the range of validity for all such experimental results may extend only to the limited sub-set of cancer cells selected for study. Virtually any factor is just a mutation away from irrelevance. It is generally possible to select clones of cancer cells that express contradictory properties. This is a frequent occurrence in patients. The capacity to develop drug resistance is a consistent property of cancer cell populations. The most striking examples are cancer cells that grow better, or require, the presence of anticancer drugs for growth, as observed with hydroxyflutamide and tamoxifen. 16,17 Even major landmarks along the pathways of tumor cell evolution are unpredictable. For example, in a study of 106 patients with colon cancer only 6.6% of tumors were found to jointly contain mutations in APC, K-ras, and p In a study of seventy-five cases of breast cancer, every patient s cancer was found to have a different set of genetic alterations. 19 Thousands of different mutant forms of the P53 gene have been catalogued in human tumors, and multiple forms have been detected in a given patient. 20,21 In principle, bulk tumor and identifiable metastatic lesions can be experimentally characterized. However, we generally cannot identify all cancer cells present in a patient with metastatic disease and we cannot know what will evolve. Tumor cell heterogeneity precludes generalization from a subset to the greater set of all tumor cells present in a patient with metastatic disease. For example, in a study of primary breast cancers, 60% displayed major intra-tumor heterogeneity in the amplification of growth regulatory genes. 22 In renal cell cancer multiple, genetically almost completely different cells exist in various locations of one and the same patient. 23 In a study of breast cancer, the genetic composition of 31% of metastatic lesions differed almost completely from that of the paired primary tumor. 24 In addition, breast cancer cells can spread to bone marrow at an early stage in tumor evolution and frequently lack genetic alterations seen in the primary tumor. 25 The analysis of tumor cell markers such as gene expression patterns and genetic alterations in a patient s tumor may provide useful information to guide in the individualization of therapy. However, the probability of success with this approach is expected to decline with increasing burden of metastatic cells and increasing genetic instability. The information required to consistently characterize all cancer cells that are present or that will evolve in a patient cannot generally be obtained by analysis of the patient s bulk tumor and identifiable metastatic lesions. *Published online at 62

3 We may be able to understand the essential molecular biology of a particular cancer cell. But it is impossible to precisely understand the molecular biology of an evolving population of cancer cells. The problem reflects the limitations of what is knowable and predictable about a complex, mathematically chaotic, indeterminate process. The target of cancer therapies must be the set of all tumor cells that are probable to evolve. Since we cannot precisely characterize all of the cancer cells that are present or predict which cancer cells will evolve in a patient with metastatic disease, the target of cancer therapies must be the set of all tumor cells that are probable to evolve. The term probable to evolve refers to all tumor cells that are present, and all tumor cells that have even a remote and clinically significant probability of evolving during the patient s illness. The requirements for the specific-cure or control of cancer are defined by properties of the set of all tumor cells that are probable to evolve. The properties of individual tumor cells are irrelevant. To cure cancer, the set of all tumor cells that are probable to evolve in the patient must be eradicated, without excessive host toxicity. To control cancer, all tumor cells that are probable to evolve must be controlled, either directly or indirectly. The probability of cure is approximately 1-pT if the set of all cancer cells with a probability of evolving p are killed and T is the cumulative tumor cell burden. (More precisely, the probability of cure is given by (1-p) T. For practical purposes and within appropriate limits this is closely approximated by the first two terms of the binomial expansion 1-pT.) If the cumulative tumor cell burden is cells, then it is necessary to (directly or indirectly) target the set of all tumor cells that each have a probability of or greater of evolving to achieve a cure rate of 99%. This assumes that all cancer cells need to be killed for cure, which may be an overly stringent assumption. 26 Therapy that incompletely targets the set of all tumor cells that are probable to evolve in a patient can act as a selective evolutionary pressure, redirect the flow of tumor cell evolution, allow resistance to develop, and fail. What can be known about the set of all tumor cells that are probable to evolve? Thus far in our analysis we have substituted the unknowable, (all that is actually present and all that will evolve), for the unknown, (all that is probable to evolve). Although, the nature of probability remains incompletely understood, as pointed out by Popper, probabilities have an objective character that describe relational properties of the physical world. 27,28 Since cancer in an individual patient is a unique, singular event, the specifics of the probability function that describes what is probable to evolve are not accessible to direct measurement. However, established scientific knowledge and principles combined with deductive logic can allow us to understand and define the set of all tumor cells that are probable to evolve in a patient. Despite the severe limitations in knowledge imposed by the stochastic nature of cancer, it is possible to formulate an empirically falsifiable, scientific theory that defines properties common to all tumor cells that are probable to evolve in a patient. Such a theory must be generally independent of specific pathways of tumor cell evolution, specific genetic alterations and specific molecular lesions that can cause cancer. It must be based on known or predictable constraints to tumor cell evolution. 29 Although water flow through the Grand Canyon is turbulent and mathematically chaotic, its course, constrained by the canyon walls, is generally quite predictable. The canyon walls that constrain the chaotic flow of tumor cell evolution are a consequence of time, the requirements for malignant behavior, the maximal rates of mutation, the laws of probability and natural selection. We cannot predict the specifics of evolution, but we can understand and predict the boundary conditions, the canyon walls. To cure or chronically control cancer we must target all that can flow within the canyon walls. Cancer cells will use almost exclusively normal cellular machinery to carry out the hallmark functions of malignancy. The set of all tumor cells that are probable to evolve will use almost exclusively normal cellular machinery to carry out malignant behavior and will retain most of the normal cellular machinery present in the stem cell of tumor origin. This follows from Darwin s Theory of Evolution and the complexity of basic life processes. The converse requires the joint occurrence of a large number of improbable events and has a *Published online at 63

4 probability of essentially zero of occurring during the time available. Cancer is a disease of cell regulation, not a re-creation of life. Cancer cell evolution occurs within the context of extensive, complex, pre-existing cellular machinery. Normal cellular machinery exists that can carry out the hallmark functions of malignancy. 30 Proliferation and invasiveness are complex, evolutionarily conserved, tightly regulated, normal cellular functions that evolved over eons of time. Invasiveness is a normal physiological activity critical to processes such as: tissue remodeling; wound healing; cell migration; blood vessel formation; trophoblast implantation; and embryonic development. The demonstration that the introduction of three oncogenes into human cells is sufficient to confer tumorigenicity to normal human cells also illustrates the point. 31 It is overwhelmingly more probable that cancer cells will use mostly normal cellular machinery than evolve extensive new machinery. Although the Rules for Making Human Tumor Cells are encoded by diverse and evolving genetic mutations, it is normal cellular machinery that must execute these instructions. 32 Cancer cells that violate this property have never been described. The complexity of the requisite interdependent biochemical processes is too great, the joint probabilities too small, and the human life span far too short for a tumor cell to evolve that violates this property. 33,34 The probability of evolutionary paths headed in the direction of violating this property will rapidly approach zero, as the evolution of each new piece of functional, tumor-specific machinery is highly improbable. Malignant behavior defines cancer. The above considerations imply that all cancer cells that are probable to evolve can be specifically detected on the basis of normal cellular machinery that enables or reflects malignant behavior, i.e., proliferation and invasiveness in an abnormal, non-physiological context. Not all cells that evolve in a tumor are malignant. Many tumor cells do not engage in malignant behavior, are end stage, and are destined to senescence or cell death. Such non-malignant tumor cells potentially can be of pathological significance through bulk effects, by producing factors that enhance the survival of malignant cells, and by causing various paraneoplastic syndromes. However, such cells cannot sustain malignant disease and in general are of little clinical significance. Information requirements for the specific identification of the set of tumor cells that are probable to evolve Tumor specificity is about information. Consider a hypothetical tumor cell detection and destruction machine that can consistently and specifically cure cancer. The machine functions by: Asking questions about the cells; Receiving bits of information in the form of answers; Deciding: normal or cancer; And killing only the cancer cells. Like Maxwell s Demon in classical thermodynamics, any such machine requires information to accomplish its task. Although the language of tumor cell identification is chemical, the principles of Information Theory still apply. 35,36 The minimum information requirements for this hypothetical machine to identify the set of tumor cells that are probable to evolve, are the essential information requirements for the specific-cure or control of cancer. What are the information requirements for any such machine or process to perform its task? Two conditions must be jointly met: specificity and comprehensiveness. Single bit questions can provide specificity, but not comprehensiveness. One bit is the information derived from a binary (yes or no) question in which the expected outcomes are equally probable to the receiver of the information. The only question that can specifically identify a cancer cell using one bit of information is the logical equivalent of: Is the cell a cancer cell? *Published online at 64

5 Or in chemical terms, a question such as: Does the cell contain a pre-defined, known, tumor specific molecular target? (There are other logical equivalents, such as: Does the cell lack a pre-defined normal protein that is absent in tumor cells and present in normal cells?). To ask this question requires specific prior knowledge related to a pathway of tumor cell evolution. Comprehensiveness based on single bit questions would require specific a priori knowledge related to all clinically probable pathways of tumor cell evolution. It is extraordinarily impractical and improbable to consistently and comprehensively characterize the set of tumor cells that are probable to evolve based on mutant tumor specific lesions or pathways of tumor cell evolution. Consider the case of chronic myelogenous leukemia (CML) where initially Bcr-Abl is of etiological significance and the disease presents with very little genetic heterogeneity. 37 Although, clinical results of targeting Bcr-Abl with Gleevec are extraordinarily good, analysis of this case conveys a pessimistic message. 38,39 First, CML provides evidence that a single genetic lesion can be sufficient to cause malignancy in humans. This is an ominous observation because it suggests that a new and independent causal lesion of malignancy can arise from a single genetic event during tumor cell evolution. Even more disturbing is the progressive genetic heterogeneity that develops as CML evolves. 40,41 To date 15 different mutant forms of Bcr-Abl have been detected in patients and 112 variants have been identified or created in vitro. 42,43 With disease progression, clones evolve that are independent of Bcr-Abl. 44 Genetic heterogeneity develops in the accelerated and blast stages of CML along with Gleevec resistance. Also, genetic alterations external to the Bcr-Abl gene can profoundly alter the ability to detect and target its function. Detection or measurement of a property always occurs within a context. In cancer the context is unstable and unpredictable. For example, the ability of Gleevec to target Bcr-Abl can be abrogated by MDR gene amplification or by Bcr-Abl gene amplification. For most solid tumors the situation is even more pessimistic. At the time of detection, most solid malignancies are already characterized by extensive genetic heterogeneity. 45,46,47,48,49 We cannot target chaos. The evolutionary nature of cancer implies that the consistent and specific cure or control of metastatic cancer is virtually impossible with any therapy or process in which tumor cell identification and targeting specificity are based on logically unconnected, single bits of information. To achieve both specificity and comprehensiveness requires multiple, logically-connected questions, related to normal cellular machinery that effects or reflects malignant behavior. Consider, for example, the following multiple-bit question: Does the cell have a pre-defined, normal protein that is characteristic of cell proliferation, and another pre-defined, normal protein that is characteristic of tissue invasiveness, and another predefined, normal protein that is characteristic of the tissue of tumor origin? The information derived from this multiple-bit question can provide tumor specificity, but not comprehensiveness. (The requirements for comprehensiveness are discussed in a latter section.) Asking this multiplebit question requires specific prior knowledge related to normal proteins and normal cellular functions. In contrast, tumor specificity based on single-bit questions requires prior knowledge related to stochastic pathways of tumor cell evolution. Other multiple-bit questions that detect malignant behavior can also provide tumor specificity. For example, the multiple-bit questions can relate to the location, microenvironment of the cell and bio-molecules other than proteins. The best questions relate to evolutionarily conserved normal cellular machinery that is essential to carry out malignant behavior. Asking a multiple-bit question is logically equivalent to detecting a pattern. The process of asking the prior set of questions is logically equivalent to detecting the presence or absence of a corresponding pattern of proteins. Tumor specificity resides in the pattern, not in the individual normal proteins that comprise the pattern. There are, in general, no single-bit questions that can detect malignant behavior on the basis of a single type of normal cellular component. By definition, normal cellular components are not tumor specific. Rare *Published online at 65

6 practical exceptions include fetal proteins and normal proteins that may be massively over-expressed by some tumor cells. However, these exceptions do not provide a basis for comprehensively identifying or targeting the set of all tumor cells that are probable to evolve in a patient. The component functions that comprise malignant behavior (proliferation, migration, invasiveness, etc.) are normal cellular functions, carried out mostly by normal cellular machinery. The expression of these normal functions in an abnormal context or abnormal cellular setting can be tumor specific. The context is key. To attain tumor specificity, these defining properties must be targeted only within the abnormal context. To do this, there is an absolute requirement for some form of multiple-bit or pattern recognition tumor targeting. Comprehensiveness requires multiple patterns. The information from one multiple-bit question can provide specificity, but cannot provide comprehensiveness. The ability to detect any single protein or single pattern can be lost by mutation. However, the probability of evading detection can be made clinically insignificant by increasing the number of independent, multiple-bit questions asked, or patterns targeted. Comprehensiveness requires targeting enough patterns so that the probability that a given tumor cell will lack all of the patterns is less than approximately This is required because a patient with cancer can have a cumulative tumor cell burden of approximately cells. The probability of pattern loss The probability of genetic and epigenetic events that confer drug resistance or protein loss in cancer cells is typically in the range of 10-8 to 10-3 per cell division. However, in some tumors the rate of chromosomal abnormalities can be in the range of 10-2 to 10-4 per chromosome per cell division. 50 Despite these high rates relative karotypic stability is observed over many generations in cell culture. 51 This implies that most of the aberrations impair clonal fitness. The probability of pattern loss should similarly be in the approximate range of 10-8 to 10-3 per cell division. The estimated probability that 10 such independent properties will all be lost is approximately to However, independence is not likely. Seemingly independent properties can become coupled during tumor cell evolution. For example, rearrangements can occur and whole chromosomes can be lost. Enough patterns must be simultaneously targeted so that these and other uncertainties are inconsequential. The specifics of the probability functions that describe tumor cell evolution are unknowable and unimportant, provided we consider events at the extreme limits of improbability. Targeting must be highly redundant. The actual number of simultaneously targeted patterns should be greater than the estimated requirements based on probable worse case scenarios. The key is to target a sufficiently large number of patterns so that the evolution of tumor cells without at least one of the patterns is too improbable to occur in patients. Targeting even one additional pattern could decrease the probability of a tumor cell evolving with the ability to evade destruction by orders of magnitude. We cannot predict which patterns will be randomly lost. But we can select a set of patterns for which it is sufficiently improbable that all will be jointly lost. The mechanisms of cell killing must also highly redundant. Targeting specificity can enable this redundancy without excessive patient toxicity. For practical purposes, comprehensiveness will require the simultaneous targeting of approximately 5 to 10 independent patterns of normal proteins that enable or reflect malignant behavior. In practice, this will mean the combined administration of approximately 5 10 drugs, targeted to 5-10 tumor specific patterns of normal proteins. Sequential drug therapy is also possible provided that the time intervals between the administration of different drugs are sufficiently short to preclude significant expansion of clones resistant to the individual drugs. The selection and targeting of a small number of simple patterns of normal proteins that enable or reflect malignant behavior is a solvable engineering problem. *Published online at 66

7 Tumor Specific Patterns Simple patterns comprised of two or more types of proteins can be highly tumor or tissue specific. The elements that comprise target patterns can be located in the tumor microenvironment, on the surface of tumor cells, inside the tumor cells, or combinations of the above. Many simple patterns of normal proteins are currently known that are highly tumor specific. For example, the pattern comprised of A33 antigen and urokinase or upar, should be highly specific for gastrointestinal neoplasms and malignancies. A33 is a differentiation antigen and normal protein that is present on epithelial cells in the GI tract and, essentially, nowhere else in the body. 52 A33 antigen expression was observed in 86/90 cases of colon cancer. Urokinase and upar are present in some normal tissues, such as the kidneys, and are over-expressed on many types of cancer cells. Urokinase receptor mrna expression was detected by in situ hybridizations in 68/80 cases of invasive colon cancer. 53 Urokinase was detected in 57/97 cases of colorectal carcinomas. 54 Urokinase may play a normal physiological role in the shedding of apoptotic or nonviable, surface, intestinal epithelial cells at the tip of villi. However, urokinase and its receptor upar are not present or are expressed only at low levels on normal, viable, intestinal epithelial cells. 55 Therefore, the pattern of both A33 antigen and urokinase or upar should be highly specific for gastrointestinal neoplasms and colon cancer. Other potential targeting patterns for colon cancer include: A33 and MMP-7; A33 and MMP-1; A33 and MMP-14; A33 and heparanase; and A33 and C-Met. MMP-1, MMP-7, heparanase, and C-met are not expressed, or expressed only at very low levels on normal colonic epithelial cells, but strongly expressed by colon cancer cells. Guanylyl cyclase C, like A33 antigen has a highly restricted distribution and could be substituted for A33 antigen in the above patterns. Elements of target patterns common to all forms of cancer All cancers are different. However, tumor cell evolution implies that a common set of normal cellular machinery effects malignant behavior. In this respect, cancer is one disease. This means that a set of tumor specific patterns of normal proteins exists that is common to all types of solid cancers. Therefore, it should be possible to develop a set of tumor specific drugs that will be effective against all types of solid cancers. Evolutionarily conserved normal proteins required for cell proliferation and invasiveness are compelling elements of targeting patterns. Two or three appropriately selected proteins should be sufficient to define each tumor-specific targeting pattern. For practical purposes, the targeting patterns need not be absolutely tumor specific, but must be absent from vital normal tissues. Optimal sets of target patterns for cancers remain to be defined. Comprehensive data is available only for a small number of patterns comprised of elements that have been individually well characterized in terms of anatomical distribution. The following sections illustrate the enormous opportunities and options available for tumor specific targeting within the context of simple tumor specific patterns of normal molecules. Markers of proliferation Cell proliferation is a highly complex and tightly coordinated process. Over 850 genes have been identified which are regulated in a cell cycle specific fashion. 56 Accordingly, there are a large number of ways in which the question; Does a cell have a marker characteristic of proliferation? can be asked. Essential or critical machinery of cell replication includes: Key enzymes in deoxyribonucleotide triphosphate metabolism Components of the pre-replicative complex Components of DNA replication Components of centrosomes and mitotic spindles Components required for cytokinesis MCM proteins are especially compelling components of targeting patterns. These proteins are required for the licensing of DNA replication and absolutely essential for cell proliferation. 57 MCM proteins are characteristic of cells with the potential for replication, not just actively replicating cells. Normal cellular differen- *Published online at 67

8 tiation and senescence is accompanied by the loss of MCM expression. Malignant cells consistently express MCM proteins. 58 Patterns comprised of MCM proteins and markers of invasiveness offer extraordinary promise for tumor targeting. Markers of invasiveness Cancer is always associated with changes in the microenvironment that reflect a disruption of the normal tissue architecture and degradation of the normal basement membranes. The synthesis of new extracellular matrix and angiogenesis are also consistent features. Both physiological and malignant invasiveness are accompanied by the concomitant expression and activation of multiple proteins. The biochemical processes that enable invasiveness are redundant. No single factor appears to be absolutely required for, and characteristic of, invasiveness. However, there are limits to the redundancy. For example, MMP inhibition in the setting of plasminogen deficiency inhibits keratinocyte migration and completely prevents wound healing in mice. 59 This also results in impaired placental vascularization and embryonic lethality. Many of the proteases involved in tumor invasiveness are produced by normal stromal cells and become tumor cell associated or enriched in the tumor cell microenvironment. This makes mutational loss highly improbable and confers robustness to protease expression. Components of normal cellular machinery that effect or reflect physiological and malignant invasiveness include: Urokinase, upar, PAI-1 MMP-1,2,3,7,8,9,10,11, 13,14, TIMP-1, TIMP-2 C-MET, HGF, RON, PAR-1, PAR-2 Plasmin Legumain EMMPRIN Fibroblast Activation Protein VEGF Tissue Factor Heparanase, hyaluronanidase Cathepsins Focal adhesion kinase 67kDA laminin receptor Accordingly, there are a large number of ways in which the question; Does the cell have a protein characteristic of invasiveness? can be asked and tumor cell invasiveness detected. None of the above proteins is individually a specific marker for invasiveness. For example, C-MET is highly expressed on normal hepatocytes and on hepatocellular carcinoma cells. 60 However, the pattern of C-MET and MCM-2 is absent from normal hepatocytes and expected in hepatocellular carcinoma, since MCM2 is required for DNA replication. Tissue Specific or Tissue Selective Markers Proteins characteristic of the tissue of tumor origin can also be included into targeting patterns. Examples include: A33 antigen and colon cancer, Prostate specific membrane antigen for prostate cancer, and Ep- CAM for epithelial malignancies. However, proteins that are not involved in executing the mechanisms of malignant behavior have a higher probability of being lost during tumor cell evolution. Tissue-specific proteins are often under the control of common regulatory elements. Mutational or epigenetic inactivation of the common regulatory elements can cause the joint loss of multiple differentiation related proteins. In addition it is common for cancer cells to loose massive portions of chromosomes, or even whole chromosomes. For these reasons, tissue specific proteins not involved in the execution of malignant behavior, are less robust as elements of targeting patterns. The analysis of pattern expression in a patient s bulk tumor and identifiable metastatic lesions may provide valuable information to guide in the selection of target patterns for the individual. However, as discussed *Published online at 68

9 previously such information will generally be insufficient to provide a consistent basis for comprehensive targeting. The canyon walls that constrain the chaotic flow of tumor cell evolution are the patterns of normal cellular machinery that effect and reflect malignant behavior. Tumor cell evolution implies that to specifically cure or control cancer multiple patterns must be targeted. A comprehensive analysis of pattern expression in a wide range of normal and malignant tissues is urgently needed. Detailed information is needed on the location and activation status of the individual proteins to allow the selection of optimal target patterns. This information can be rapidly obtained using existing technology. Pattern recognition tumor targeting technologies that kill cells if and only if the cells express the targeted patterns are also absolutely essential. Mechanisms of Pattern Recognition Tumor Targeting Pattern recognition tumor targeting requires drugs that are able to detect multiple inputs corresponding to the individual components of the pattern, and kill cells if and only if the complete pattern is detected. This requires multi-functionality in drug design. A number of different technologies are required since target patterns may be comprised of combinations of elements that are located in the tumor microenvironment, on tumor cells, or inside tumor cells. Also the concentration of different elements in target patterns may vary over orders of magnitude. A variety of pattern recognition targeting technologies can be developed using modular building blocks to yield drugs that ask multiple-bit questions to decide: Normal or Cancer; Spare or Kill. Key chemical components of pattern recognition targeting technologies include: Targeting ligands: Chemical groups that specifically bind to a target receptor Triggers: Chemical groups that can undergo specific bio-transformation and change the chemistry and functionality of the drug Linkers: Relatively inert components that connect different groups in a molecule Effector agents: Groups that exert the desired pharmacological effect such as cytotoxicity Male ligands and female adaptors: Chemical groups that can bind specifically and with high affinity to each other Additional functionality can be added in a modular fashion if required to address issues such as: intracellular transport; intracellular trapping; and solubility. There are many different approaches to pattern recognition tumor targeting, including: Pairs of Independently Targeted Synergistic Agents Pattern Recognition Based on Multi-Site Binding Exponential Pattern Recognition Targeting Independently Targeted Synergistic Agents The approach is illustrated in Figure 1. In this approach Agent 1 and Agent 2 are individually nontoxic or of low toxicity but in combination highly toxic. Only cells that express the target pattern of both red and green receptors will be killed. Many pairs of synergistic agents are potentially suitable. There are a large number of agents that in combination can exert potent synergistic toxicity that could be employed in this approach to pattern recognition targeting. General classes include: 1. Inhibitors of synthetic and salvage pathways for essential cell components such as thymidine; 2. Inducers of ceramide and inhibitors of ceramide degradation; 3. Agent 1 as an enzyme that activates agent 2; 4. Inducers of DNA damage and inhibitors of DNA repair; 5. Hydrogen peroxide generating systems and catalysts of free radical production; 6. Ionophores and inhibitors of ion pumps that maintain ion gradients *Published online at 69

10 Many paired mutations are known to confer synthetic lethality in yeast. Major screening programs are ongoing in human cells that will certainly identify many additional suitable sites of action to exploit in the above approach. 61 Agent 1 Nontoxic Agent 2 Nontoxic Agent 1 + Agent 2 Toxic Pattern Recognition targeting based on multi-site binding Targeting Ligand Agent 1 Agent 1 Agent 2 Nature provides an example that Targeting represents nearly perfect, almost theoretically ideal molecular targeting: Normal cell Tumor cell Receptor anaphylactic shock. Incredibly minute Type A concentrations of antigens can elicit histamine release from mast cells and trigger anaphylaxis. The mechanism is multivalent binding of the antigen to cell surface receptors. This same mechanism can be exploited to develop tumor-specific anticancer drugs that approach the limits of ideal targeting. Agent 2 Normal cell Type B No Toxicity Toxicity No Toxicity Figure 1. Pattern Recognition Tumor Targeting with Independently Targeted Synergistic Agents The following sections describe some approaches to Pattern Recognition Tumor Targeting that have the potential to enable dose reductions of as a much as one million times relative to non-targeted chemotherapy and thousands of times relative to current targeted approaches. The enormous difference in functional affinity possible between mono-valent and multi-valent binding can provide a basis for pattern recognition targeting. This approach is illustrated in Figure 2. The presence of multiple targeting ligands can enable the drug to engage in multi-site binding and endow the drug with pattern recognition capability. Multi-site binding can increase the functional affinity of binding by thousands of times. However, this enhancement of affinity can occur only if the cell expresses the targeting receptor pattern required for multi-site binding. The ability of multi-site binding to markedly increase functional binding affinity is extensively utilized by nature. 62 Examples include: immunoglobulin molecules; 63 complement C1q; 64 antigen mediated histamine Tumor cell Normal cell High affinity binding Lower affinity binding Figure 2. Pattern Recognition Tumor Targeting with Independently Targeted Synergistic Agents *Published online at 70

11 release from basophils; 65,66 and shiga like toxins and antagonists. 67 An increase in functional binding affinity of 200,000 times (compared to single site binding) has been described for peptabodies, a class of proteins with five binding sites. 68 A ten billion times increase in affinity has been reported for the binding of a trimer of vancomycin to a trimer of the dipeptide d-ala-d-ala. 69 The enormous increase in binding affinity that can result from multivalent binding can enable pharmacological activity at extraordinarily low concentrations. Histamine release from basophils, due to two site binding of bivalent haptens and cross-linking of cell surface IgE molecules, has been reported at concentrations as low as M. 70 When an asymmetric bivalent hapten was employed, histamine release was only detected if the cell had the pattern of surface IgE molecules specific for both different haptens that comprised the bivalent ligand. 71 Nature has provided a wonderful mechanism in multivalent interactions that can be utilized to develop anticancer drugs of both extraordinarily high target affinity and pattern recognition capabilities. The binding of a bivalent ligand to cell surface receptors will occur in two steps: with one site binding followed by binding at the second site. The targeting receptors need not be in close proximity as most proteins are mobile on the cell surface. Exponential Pattern Recognition Tumor Targeting Exponential Pattern Recognition Tumor Targeting is a method of multiple-bit targeting designed to enable a massive exponential amplification of the quantity of anticancer drugs that are specifically delivered to tumor cells. Targeting specificity is for the pattern of a targeting receptor and a triggering enzyme. Exponential Pattern Recognition Targeting is the drug delivery equivalent of the polymerase chain reaction (PCR). In effect from one targeting receptor two are created, from two four, etc. Massive amplification of drug delivery to target patterns should be possible. In this method two compounds are co-administered to the patient (See Figure 3). Compound 1 is a drug molecule that is comprised of one or more targeting ligands that can bind specifically and tightly at low concentrations to a targeting receptor(s) on the surface of the tumor cell and a masked female adapter. The masked female adapter is a chemical group which, when unmasked, can bind tightly and specifically to the male ligand. Compound 2 is a drug molecule that is comprised of a male ligand that can bind to the unmasked female adapter, a toxic anticancer drug, and two or more masked female adapters. The mechanisms of action of Exponential Pattern Recognition Targeting are described below and illustrated in Figure Compound 1 binds with high affinity to the tumor cell. 2. The masked female adapter is unmasked by the triggering enzyme. 3. A molecule of Compound 2 binds to the unmasked female adapter. 4. The triggering enzyme then unmasks the two female adapters. Additional cycles of binding and unmasking of the female adapters will deposit large quantities of toxin in a tree-like structure on the tumor cell surface as shown in the Figure 4. *Published online at 71

12 Compound 1 Compound 2 Masking Group Masked Female Adapter Targeting Ligand Male Ligand Triggering Enzyme Unmasked Female Adaptor The masked female adaptors are specifically unmasked by the Triggering Enzyme. The male and female parts are selected to bind with very high affinity. Figure 3. Key Components of Exponential Pattern Recognition Tumor Targeting. *Published online at 72

13 Compound 1 Compound 2 Unmasked Female Adaptor Triggering-Enzyme Tumor cell 1 Tumor cell 2 Tumor cell Two Unmasked Female Adaptors 3 Compound Triggering-Enzyme 5 Tumor cell Tumor cell After Multiple Cycles: Tumor cell Figure 4. Exponential Patten Recognition Targeting *Published online at 73

14 Triggering Enzymes Targeting specificity in Exponential Pattern Recognition Tumor Targeting derives from the pattern comprised of the targeting receptors on the cell surface and the triggering enzyme. A broad range of enzymes that are enriched on tumor cells or in the micro-environment of tumor cells, such as plasmin, urokinase, matrix metalloproteinases, fibroblast activation protein can be utilized to specifically trigger unmasking of the masked female adaptors. Many approaches to the design of specific enzyme triggers are known. Oligo-Nucleotide Analogs as Female Adaptors: Male ligands and female adaptors are available that bind specifically, rapidly, and essentially irreversibly. Complementary peptide oligo-nucleotide analogs are an example. 72 A wide range of chemical components based on existing technology can be utilized to mask the female adaptor. The high binding affinity between the selected male ligands and female adaptors could allow the drugs to be used at picomolar concentrations, yet massively accumulate in the tumor. Many additional methods and technologies for targeting patterns can also be developed. A more detailed description of the chemistry of Pattern Recognition Tumor Targeting and related technologies is beyond the scope of this document. Conclusions Cancer is an astronomically diverse, indeterminate, stochastic evolutionary process. To consistently and specifically cure (or control) cancer, therapy must be able to eradicate (or control) the set of all tumor cells that are probable to evolve in the patient. This requires some form of pattern recognition tumor targeting in which specificity is based on the detection of abnormal patterns of normal cellular machinery that effect or reflect malignant behavior, i.e., proliferation and invasiveness in an abnormal context. Comprehensiveness requires the simultaneous targeting of multiple patterns such that the probability of a tumor cell evolving without at least one pattern is less than approximately per cell division. For practical purposes, approximately 5 10 drugs targeted to 5-10 independent patterns will be required. Unless these requirements are satisfied, the consistent and specific cure of cancer or control of metastatic cancer is virtually impossile. The specific cure or control of metastatic cancer is a solvable engineering problem. However, a much higher degree of integration and coordination of drug development are required than exists today. The astronomy community flattened a mountaintop in the middle of the Atacama Desert of Chile and built at this remote site the four largest telescopes in the world. Each was comprised of thousands of interacting parts and all were designed to function in an integrated fashion as one super telescope. Cancer patients and their families deserve no less of an integrated, cooperative effort. With a similar degree of commitment, organization, and funding, an integrated set of drugs can rapidly be developed for the specific cure or control of metastatic cancer. We believe that a multi-institutional consortium involving, industry, academia, government, and private foundations can rapidly transform the hope of cure into reality. Acknowledgements I would like to express my thanks to the many individuals whose support, encouragement, and helpful discussions helped make the current work possible. A detailed list is too long to include. I would like to express my deepest appreciation to Dr. Emil Frei III, Physician-in-Chief, Emeritus at the Dana-Farber Cancer Institute; and Donald S. Coffey Ph.D.; Professor of Urology, Oncology, Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine. I would also like to express special thanks to Mr. Michael F. Brewer, Mr. Andrew Siciliano, Mr. Gregg Goldstein and to my parents, wife, and family. References 1. Frei E; Curative cancer chemotherapy. (1985). Cancer Res. 45(12 Pt 1): Brenner H, Hakulinen T. (2002). Up-to-date long-term survival curves of patients with cancer by period analysis. J Clin Oncol. 20(3):826 *Published online at 74

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