Conserved machine learning rankings of MYC gene combinations across different sensitivity methods connote existence of biological synergy

Conserved machine learning rankings of MYC gene combinations across different sensitivity methods connote existence of biological synergy Shriprakash Sinha Independent Researcher; Orcid ID : 0000-0001-7027-5788 104-Madhurisha Heights Phase 1, Risali, Bhilai-490006, India Abstract A recent design of a machine learning based search engine was published, that ranks combinations of genes that might be working synergistically in cells in various processes. To demonstrate its efficacy in real life scenario, the data set containing recordings of up/down regulated genes generated from colorectal cancer (CRC) cells treated with PROCN-WNT inhibitor drug ETC-1922159 was taken. The regulation of the genes were recorded individually, but in many cases, it is still not known which higher (≥2) order gene combinations might be playing a greater role in CRC. Here, I demonstrate that the rankings assigned to gene combinations at 2nd order, by the search engine are conserved across the different sensitivity methods (and kernels/variants). This conservation points to the possible existence of the synergy between genes at the biological level. To establish the hypothesis I present ranked combinations of v-myc avian myelocytomatosis viral oncogene homolog (MYC), known to encode proteins that play significant role as transcription factors in cancer and target various kinds of genes, thus contributing to regrowth and proliferation. The manuscript identifies experimentally tested combinations of MYC-X in literature (whether in CRC cell or other cancer/ordinary cell). Second, the work reveals machine learning rankings for these MYC-X combinations in ETC-1922159 treated CRC cells. For experimentally established combinations, these rankings bolster confirmatory results. Based on the second step, the work points to new rankings of unknown/untested/unexplored MYC-X combinations that might be working synergistically in CRC cells. Keywords: MYC, Gene combination, Colorectal Cancer, ETC-1922159, Machine learning. IConserved ML rankings of MYC gene combinations Email address: [email protected] (Shriprakash Sinha) 1Aspects of unpublished work were presented in a poster session at the first Wnt Gordon Research Conference, from 6-11 August 2017, held in Stowe, VT 05672, USA. Preprint submitted to Preprint January 24, 2025 Contents 1 Significance 3 2 Introduction 3 2.1 Combinatorial search problem and a possible solution . . . . . . . . . 3 2.2 Insight behind the work . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 PORCN-WNT inhibitors . . . . . . . . . . . . . . . . . . . . . . . . 4 2.4 MYC .................................. 4 3 Tools of study 5 3.1 Sensitivityanalysis ........................... 5 3.1.1 Variance based Sobol method . . . . . . . . . . . . . . . . . 5 3.1.2 Density based HSIC method . . . . . . . . . . . . . . . . . . 6 3.1.3 Sensitivity package in R . . . . . . . . . . . . . . . . . . . . 6 4 Static data by [2] 7 5 Methodology 7 5.1 Revealing higher order biological hypotheses via sensitivity analysis and insilico ranking algorithm . . . . . . . . . . . . . . . . . . . . . 7 5.2 Design for static data from [2] . . . . . . . . . . . . . . . . . . . . . 8 6 Results & discussion 9 6.1 How to interpret the ranking? . . . . . . . . . . . . . . . . . . . . . . 9 6.2 Conserved rankings of experimentally established and unexplored/untested, MYC-Xcombinations......................... 10 6.2.1 Adehesion / matrix / tissue remodeling (AMT) . . . . . . . . 10 6.2.2 Ligand / receptor (LR) . . . . . . . . . . . . . . . . . . . . . 12 6.2.3 Structural (STR) . . . . . . . . . . . . . . . . . . . . . . . . 15 6.2.4 Channels / components (CC) . . . . . . . . . . . . . . . . . . 17 6.2.5 Chaperone / protein folding (CPF) . . . . . . . . . . . . . . . 18 6.2.6 Translation / ribosomal protein (TRP) . . . . . . . . . . . . . 20 6.2.7 Vesicle protein / trafficking (VPT) . . . . . . . . . . . . . . . 24 6.2.8 Carbohydrate (CH) . . . . . . . . . . . . . . . . . . . . . . . 25 6.2.9 Energy metabolism (EM) . . . . . . . . . . . . . . . . . . . . 27 6.2.10 Lipid(LPD)........................... 29 6.2.11 Nucleotide (NTD) . . . . . . . . . . . . . . . . . . . . . . . 30 6.2.12 Signal transduction (STD) . . . . . . . . . . . . . . . . . . . 32 6.2.13 Nuclear regulatory factors (NRF) . . . . . . . . . . . . . . . 34 6.2.14 Nucleolus / RNA-binding protein (NRBP) . . . . . . . . . . . 36 6.2.15 Transcription factors (TF) . . . . . . . . . . . . . . . . . . . 39 6.2.16 DNA maintenance / repair (DMR) . . . . . . . . . . . . . . . 42 6.2.17 Other(OT) ........................... 46 7 Conclusion 47 2 8 Conflict of interest 47 9 Author’s contributions 48 10 Acknowledgements 48 11 Code Availability 48 12 Source of Data 48 13 References 48 1. Significance A search engine was used to reveal and prioritise gene combinations, by adapting the code from a recently published work. Rankings of combinations were observed to be conserved across the different sensitivity methods (used to estimate the influence of components of a combination). This points to possible existence of synergy between genes at biological level. Presented here are rankings of experimentally established combinations of MYC-X for CRC treated with ETC-1922159 and rankings that are unexplored. These point to efficacy and potential of the search engine. The engine is effective for ranking combinations of any gene of choice. 2. Introduction 2.1. Combinatorial search problem and a possible solution A recent design of a machine learning based search engine was published [1], that ranks combinations of genes that might be working synergistically in cells in various processes. To demostrate its efficacy in real life scenario, the data set containing recordings of up/down regulated genes generated from colorectal cancer (CRC) cells treated with PROCN-WNT inhibitor drug ETC-1922159 was taken [2]. The regulation of the genes were recorded individually, but in many cases, it is still not known which higher (≥2) order gene combinations might be playing a greater role in CRC. Here, I demonstrate that the rankings assigned to gene combinations at 2nd order, by the search engine are conserved across the different sensitivity methods (and kernels/variants). This conservation points to the possible existance of the synergy between genes at the biological level. Readers are requested to go through the adaptation of the above mentioned work for gaining deeper insight into the working of the pipeline and its use of published data set generated after administration of ETC-1922159, [3]. 2.2. Insight behind the work Across all search engines has the fundamental principle remains the same i.e to capture the pattern available in the data and based on that pattern, rank a list of queries. 3 Different algorithms can be applied, however if the fundamental pattern is captured accurately, then the rankings will remain approximately the same, with slight variations, across the different kinds of search engines used. I use one search engine, however, vary the way the patterns are captured via use of different sensitivity methods. Each sensitivity method uses a different flavour/mathematical formulation to compute the sensitivity indices to estimate the influence of the involved factors. These involved factors are genes that play a role in cell biology, in the above research. The insight is that all methods will capture the sensitivity of the involved factors based on their recorded regulations and the search engine will rank the combination of factors based on these sensitivity indices. Since the role of involved factors are captured properly, the search engine will give appropriate rankings to the combinations, thus capturing which gene combinations might be playing significantly in a biological phenomena. The above work shows rankings for experimentally confirmed combinations as well as unexplored/untested combinations. These rankings are not just numbers. They point to the existence of biological synergy in the form of gene combinations, whether tested in wet lab or unexplored till now. Finally, the findings suggest that the rankings are conserved across the different sensitivity methods used. 2.3. PORCN-WNT inhibitors The regulation of the Wnt pathway is dependent on the production and secretion of the WNT proteins. Thus, the inhibition of a causal factor like PORCN which contributes to the WNT secretion has been proposed to be a way to interfere with the Wnt cascade, which might result in the growth of tumor. Several groups have been engaged in such studies and known PORCN-WNT inhibitors that have been made available till now are IWP-L6 [4] & [5], C59 [6], LGK974 [7] and ETC-1922159 [8]. In this study, the focus of the attention is on the implications of the ETC-1922159, after the drug has been administered. The drug is a enantiomer with a nanomolar activity and excellent bioavailability as claimed in [8]. 2.4. MYC c-MYC (MYC) is a gene that encodes for transcription factor. It belongs to the family of MYC genes which includes B-MYC, N-MYC, L-MYC and S-MYC. Mutations in c-MYC have been found to be involved in various kinds of cancers. MYC (or v-myc avian myelocytomatosis viral oncogene homolog) contains a helix-loop-helix (HLH) and leucine zipper (LZ) domain that helps it to bind to interact with DNA and form a complex with MAX [9], respectively. A good introductory review on c-MYC can be found in [10] which covers topics on transcription factors and binding sites, transcriptional properties, MYC affected genes and finally role of MYC in cell cycle, apoptosis [11] and metabolism [12]. A lot of work has been done in MYC at various levels as it is implicated in various types of cancer. Additionally, MYC has been found to have influence on the chromatin structure also [13]. In colon cancer, c-MYC has been found to be highly expressed [14] & [15]. In colorectal cancer cells treated with ETC1922159, MYC was found to be down regulated along with other genes. In this study, I use MYC as a choice of gene because it is induced by mitogenic signals and regulates 4 downstream cellular responses. Overexpressed MYC promotes malignant transformation and modulates expression of a range of genes in experimental systems, however only a few are proven direct targets. For curating experimentally established combinations of MYC, I use the list tabulated by [16]. They present a large-scale screen for MYC-binding sites in live human cells by identifying genes directly bound by MYC through the consensus E-box element CACGTG. Their strategy was based on the preselection of candidate sites with bioinformatic tools, which was followed by the experimental analysis of a large number of individual sites with a quantitative ChIP assay. They identify 257 genes within the high-affinity group of MYC-targets and assign them to a functional category. The genes qualify as high-affinity targets in all cell lines tested. 3. Tools of study Sensitivity analysis and its relevance in systems biology have been covered in a recently published article [17], which forms the foundation for this work. In this work, the sensitivity indices are computed for all factors or combination of factors affecting the pathway. Ranking using support vector machines (SVM) are then employed using these sensitivity indices. The work uses SVM package by [18] in https://www.cs. cornell.edu/people/tj/svm_light/svm_rank.html. I use the adaptation of the above engine to rank 2nd order gene combinations. 3.1. Sensitivity analysis For completeness, a part of [17] has been reproduced below. 3.1.1. Variance based Sobol method Seminal work by Russian mathematician [19] lead to development as well as employment of SA methods to study various complex systems where it was tough to measure the contribution of various input parameters in the behaviour of the output. A recent unpublished review on the global SA methods by [20] categorically delineates these methods with the following functionality •screening for sorting influential measures ([21] method, Group screening in [22] & [23], Iterated factorial design in [24], Sequential bifurcation design in [25] and [26]), •quantitative indicies for measuring the importance of contributing input factors in linear models ([27], [28], [29] and [30]) and nonlinear models ([31], [32], [33], [34], [35], [36], [37], & [38], [39], [40], [41], [42], [43], [44], [45], [46] and [47]) and •exploring the model behaviour over a range on input values ([48] and [49], [50] and [51]). [20] also provide various criteria in a flowchart for adapting a method or a combination of the methods for sensitivity analysis. The general idea for variance based Sobol method is as follows - A model could be represented as a mathematical function with a multidimensional input vector where each element of a vector is an input factor. This function needs to be defined in a unit dimensional cube. Based on ANOVA decomposition, the function can then be broken 5 down into f0and summands of different dimensions, if f0is a constant and integral of summands with respect to their own variables is 0. This implies that orthogonality follows in between two functions of different dimensions, if at least one of the variables is not repeated. By applying these properties, it is possible to show that the function can be written into a unique expansion. Next, assuming that the function is square integrable variances can be computed. The ratio of variance of a group of input factors to the variance of the total set of input factors constitute the sensitivity index of a particular group. 3.1.2. Density based HSIC method Besides the above [19]’s variance based indicies, more recent developments regarding new indicies based on density, derivative and goal-oriented can be found in [52], [53] and [54], respectively. In a latest development, [55] propose new class of indicies based on density ratio estimation [52] that are special cases of dependence measures. This in turn helps in exploiting measures like distance correlation [56] and HilbertSchmidt independence criterion [57] as new sensitivity indicies. The framework of these indicies is based on use of [58] f-divergence, concept of dissimilarity measure and kernel trick [59]. Finally, [55] propose feature selection as an alternative to screening methods in sensitivity analysis. The main issue with variance based indicies [19] is that even though they capture importance information regarding the contribution of the input factors, they •do not handle multivariate random variables easily and •are only invariant under linear transformations. In comparison to these variance methods, the newly proposed indicies based on density estimations [52] and dependence measures are more robust. The general idea for density based HSIC method is as follows - The sensitivity index is actually a distance correlation which incorporates the kernel based HilbertSchmidt Information Criterion between two input vectors in higher dimension. The criterion is nothing but the Hilbert-Schmidt norm of cross-covariance operator which generalizes the covariance matrix by representing higher order correlations between the input vectors through nonlinear kernels. For every operator and provided the sum converges, the Hilbert-Schmidt norm is the dot product of the orthonormal bases. For a finite dimensional input vectors, the Hilbert-Schmidt Information Criterion estimator is a trace of product of two kernel matrices (or the Gram matrices) with a centering matrix such that HSIC evalutes to a summation of different kernel values. It is this strength of the kernel methods that HSIC is able to capture the deep nonlinearities in the biological data and provide reasonable information regarding the degree of influence of the involved factors within the pathway. Improvements in variance based methods also provide ways to cope with these nonlinearities but do not exploit the available strength of kernel methods. Results in the later sections provide experimental evidence for the same. 3.1.3. Sensitivity package in R The sensitivity package by [60] was used to develop the search engine pipeline. The current research uses the Hilbert Schmidt Independence Criterion (HSIC) and SOBOL 6 method, implemented in the sensitivity package mentioned above. I use three different kernels under the HSIC method namely, •laplace, •linear and •rbf. For SOBOL method, I use •sobol-2002, and •sobol-jansen. Each of these variants or kernels have been implemented in the sensitivity package and option has been provided in the search engine code to generate the rankings for a particular gene, using a choice of a kernel/variant at a time. Technical details about the variants and kernels can be found in references cited in [60]. 4. Static data by [2] Data used in this research work was released in a publication by [2]. The ETC-1922159 was released in Singapore in July 2015 under the flagship of the Agency for Science, Technology and Research (A*STAR) and Duke-National University of Singapore Graduate Medical School (Duke-NUS). Note that the ETC-1922159 data show numerical point measurements that is as [2] quote - ”List of differentially expressed genes identified at three days after the start of ETC-159 treatment of colorectal tumors. Log2 fold-changes between untreated (vehicle, VEH) and ETC-159 treated (ETC) tumors are reported.” The numerical point measurements of differentially expressed genes were recorded using the following formulation of fold changes in equation 1 (see [61], [62] and [63]). log2 VEHavg ETCavg (1) 5. Methodology 5.1. Revealing higher order biological hypotheses via sensitivity analysis and insilico ranking algorithm In the trial experiments on ETC-1922159 [2], a list of genes (2500±) have been reported to be up and down regulated after the drug treatment and a time buffer of 3 days. Some of the transcript levels of these genes have been recorded and the experimental design is explained elaborately in the same manuscript. In the list are also available unknown or uncharacterised proteins that have been recorded after the drug was administered. These have been marked as ”- -” in the list (Note - In this manuscript these uncharacterised proteins have been marked as ”XXM”, were M= 1,2,3, ...). The aim of this work is to reveal unknown/unexplored/untested biological hypotheses that form higher order combinations. For example, it is known that the combinations of WNT-FZD or RSPO-LGR-RNF play significant roles in the Wnt pathway. But the n≥2,3, ...-order combinations out of N(>n)genes forms a vast combinatorial search forest that is extremely tough to investigate due to the humongous amount of combinations. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or random choices to investigate a particular combinatorial hypothesis. The current work aims to reveal these unknown/unexplored/untested combinations by prioritising these combinations 7 using a potent support vector ranking algorithm [18]. This cuts down the cost in time/energy/investment for any investigation concerning a biological hypothesis in a vast search space. The pipleline works by computing sensitivity indicies for each of these combinations and then vectorising these indices to connote and form discriminative feature vector for each combination. The ranking algorithm is then applied to a set of combinations/sensitivity index vectors and a ranking score is generated. Sorting these scores leads to prioritization of the combinations. Note that these combinations are now ranked and give the biologists a chance to narrow down their focus on crucial biological hypotheses in the form of combinations which the biologists might want to test. Analogous to the webpage search engine, where the click of a button for a few key-words leads to a ranked list of web links, the pipeline uses sensitivity indices as an indicator of the strength of the influence of factors or their combinations, as a criteria to rank the combinations. 5.2. Design for static data from [2] The procedure begins with the listing of all Cn kcombinations for knumber of genes from a total of ngenes. Here ncan be the choice of the biologist. kis ≥2 and ≤(n− 1). Each of the combination of order krepresent a unique set of interaction between the involved genetic factors. Note that the ETC-1922159 data show numerical point measurements that is as [2] quote ”List of differentially expressed genes identified at three days after the start of ETC-159 treatment of colorectal tumors. Log2 fold-changes between untreated (vehicle, VEH) and ETC-159 treated (ETC) tumors are reported.” Since the sensitivity analysis methods require a sample for a particular observation, a steep gaussian distribution was generated with a jitter (noise) added to the deviation from the reported point measurement of 0.005. In this experiment, the distribution contained 10 measurements (including the point of measurement under consideration). This is repeated for each point of measurement. To have an averaged ranking, the experiment was designed to run for 50 iterations. In each iteration, the datasets are combined in a specifed format which go as input as per the requirement of a particular sensitivity analysis method. Thus for each pth combination in Cn kcombinations, the dataset is prepared in a required format (See .R code in mainscript-2-2.R). Details of formatting the data have not been presented in the article to maintain the fluidity and brevity. Interested readers can find examples of formating the data in the sensitivity analysis package in R. After the data has been transformed, vectorized programming is employed for density based sensitivity analysis and looping is employed for variance based sensitivity analysis to compute the required sensitivity indices for each of the pcombinations. After the above sensitivity indices have been stored for each of the pth combination, for a chosen sensitivity analysis method, the next step in the design of experiment is conducted. Here, the indices are averaged per combination to have a mean index value. These index values form the discriminative features for a particular combination. For a kth order combination, a vector of kelements or indices forms a feature vector. Thus for Cn kcombinations there will be Cn kvectors, each containing kelements. Next, SV MRank learn [18] is used to generate a model on default value Cvalue of 20. In the 8 current experiment on toy model Cvalue has not been tunned. The training set helps in the generation of the model as the different gene combinations are numbered in order which are used as rank indices. The model is then used to generate score on the observations in the testing set using the SVMRank classi f y [18]. This is followed by sorting of these scores along with the rank indices already assigned to the gene combinations. The end result is a sorted order of the gene combinations based on the ranking score learned by the SV MRank algorithm. Note that the following is the order in which the files should be executed in R, in order, for obtaining the desired results (Note that the code will not be explained here) - • use source(”extractETCdata.R”) •use source(”mainScript-2-2.R”) •use source(”SVMRankResults-S-mean.R”). 6. Results & discussion 6.1. How to interpret the ranking? In each of the sections below, one will find two tables. The first table lists the rankings of a particular gene combination based on these kernels. Based on majority voting, a combination is decided to be low ranked or high ranked. So, for example, if majority rankings point to low numerical value (i.e below the half way mark of approximately 2500 gene combinations), then the combination is possibly not highly ranked in colorectal cancer cells AFTER the ETC-1922159 treatment. Looking at it in another way, this low ranking suggests that the combination might have been up regulated in colorectal cancer cells BEFORE the ETC-1922159 treatment. This points to the inference that the combination of the two genes/proteins was working synergistically in colorectal cancer, while being up regulated and before ETC-1922159 treatment. The ETC-1922159 administration had caused a down regulation of genes in colorectal cancer cells and what is available as data by [2] points to down regulated recordings of genes/proteins taken individually. The second table uses the majority voting mentioned above, to filter out which combinations need to be further tests in the wet lab. These combinations recorded in the second table are inferences/pointers to existence of possible synergistic combinations that might be working in the cell, in a particular scenario (here colorectal cancer cells). Additionally, one will see two inferences - •based on the experimentally tested and established synergies in any other pathological/normal cell, if recorded in colorectal cancer cells treated with ETC-1922159, these combinations will ranked by the engine appropriately (or note - there might be a possibility that the experimentally tested combination established in a different scenario, might not get an approprate rank by the search engine in the colorectal cancer cells treated with ETC-1922159.) and •based on the cues from previous point, there will be combinations ranked by the engine, that point to new synergies that have not been explored/tested in wet lab. Further, in a list of approximately 2500 genes that were up/down regulated after ETC-1922159 treatment, for the second order combinations there will be 2499 combinations. The engine generates the ranking for all these 2499 combinations. However, it is not possible to report the ranking of all 2499 combinations in a single article, for a 9 UNEXPLORED COMBINATORIAL HYPOTHESES STR members synergy BEFORE drug treatment w.r.t EPB41L4A MYC LMNB-1/2 MYC STMN1 MYC STR members synergy AFTER drug treatment w.r.t LAMB1 MYC Table 6: 2nd order combinatorial hypotheses between MYC and STR members. 16 6.2.4. Channels / components (CC) Under this functional category in [16], the following MYC-target genes are reported - solute carrier family 4 member 2 (SLC4A2). Table 7 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 8 generated from analysis of the ranks in table 7. The table 7 shows rankings w.r.t MYC. For genes related to Solute carrier family 4 (SLC4A7); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. One can also interpret the results of the table 7 graphically, in table 8. RANKING MYC TARGETS IN CC MYC - X HSIC SOBOL laplace linear rbf 2002 jansen Solute carrier family 4 SLC4A7 - MYC 1159 282 796 541 1314 Table 7: 2nd order interaction ranking between MYC VS CC members. UNEXPLORED COMBINATORIAL HYPOTHESES CC members synergy BEFORE drug treatment w.r.t SLC4A7 MYC Table 8: 2nd order combinatorial hypotheses between MYC and CC members. 17 6.2.5. Chaperone / protein folding (CPF) Under this functional category in [16], the following MYC-target genes are reported - heat shock protein family (HSPA8, HSPE1, HSPD1 and HSPCAL3). Table 9 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 10 generated from analysis of the ranks in table 9. The table 9 shows rankings w.r.t MYC. For genes related to Heat shock protein family (i.e HSPB6, HSPE1, HSPD1, HSPA4L and HSPA9); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to Heat shock protein family (i.e HSPA4); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the table 9 graphically, in table 10. RANKING MYC TARGETS IN CPF MYC - X HSIC SOBOL laplace linear rbf 2002 jansen Heat shock ... protein family HSPB6 - MYC 193 223 111 330 645 HSPE1 - MYC 440 1041 467 1134 1095 HSPD1 - MYC 747 487 784 1023 654 HSPA4L - MYC 1085 1268 542 944 1684 HSPA9 - MYC 1481 1488 1454 2160 1719 HSPA4 - MYC 1705 768 1541 1160 1612 Table 9: 2nd order interaction ranking between MYC VS CPF members. 18 UNEXPLORED COMBINATORIAL HYPOTHESES CPF members synergy BEFORE drug treatment w.r.t HSP-B6/E1/D1/A4L/A9 MYC CPF members synergy AFTER drug treatment w.r.t HSPA4 MYC Table 10: 2nd order combinatorial hypotheses between MYC and CPF members. 19 6.2.6. Translation / ribosomal protein (TRP) Under this functional category in [16], the following MYC-target genes are reported - eukaryotic translation initiation factor (EIF-4A1/4E/5A2); Poly(A)-binding protein (PABP); ribosomal protein S (RPS-19/6) and ribosomal protein L (RPL-13A/19/22/27A). Table 11 and shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 13 generated from analysis of the ranks in table 11 and 12. The table 11 shows rankings w.r.t MYC. For genes related to Eukaryotic translation initiation factor (i.e EIF2B3 and EIF2D); Poly(A)-binding protein (i.e PABPC1L); and Ribosomal protein S (i.e RPSA, RPS2, RPS2P46, RPS3A, RPS18, RPS9 and RPS23) all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to Eukaryotic translation initiation factor (i.e EIF3L, EIF2B1, EIF2AK4, EIF4B, EIF3E, EIF3F, EIF2B5 and EIF4EBP1); Poly(A)- binding protein (i.e PABPC4) and Ribosomal protein S (i.e RPS5, RPS11, RPS27, RPS4X, RPS3, RPS6KL1, RPS27A, RPS14, RPS21, RPS24, RPS16, RPS19, RPS20, RPS13, RPS7, RPS12, RPS8, RPS29, RPS25 and RPS23); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. The table 12 shows rankings w.r.t MYC. For genes related to Ribosomal protein L (i.e RPL22, RPL5, RPL7L1, RPL21, RPL10A, RPL15, RPL19, RPL41, RPL23, RPL13A, RPL4 and RPL3) all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to Ribosomal protein L (i.e RPL24, RPL39, RPL30, RPL27A, RPL35A, RPL7, RPL7A, RPL38, RPL26, RPL23A, RPL14, RPL6, RPL11, RPL27, RPL12, RPL37A, RPL39L, RPL37, RPL36A, RPL31, RPL9, RPL35, RPL18A, RPL32, RPL34 and RPL13); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the tables 11 and 12 graphically, in table 13. 20 RANKING MYC TARGETS IN TRP MYC - X HSIC SOBOL laplace linear rbf 2002 jansen Eukaryotic translation ... initiation factor EIF2B3 - MYC 564 871 479 1294 258 EIF2D - MYC 953 1166 985 1353 1174 EIF3L - MYC 1045 1672 1300 2476 2622 EIF2B1 - MYC 1666 1679 1882 1492 2000 EIF2AK4 - MYC 1687 2013 1409 2522 2370 EIF4B - MYC 1737 1744 1799 2615 2565 EIF3E - MYC 1808 1716 1715 1707 1538 EIF3F - MYC 2135 1769 2105 1989 1645 EIF2B5 - MYC 2175 1669 2337 1957 1974 EIF4EBP1 - MYC 2210 2149 2151 1948 86 Poly(A)-binding protein PABPC4 - MYC 2288 2226 2301 2040 2073 PABPC1L - MYC 1274 242 1410 1207 2047 Ribosomal protein S RPSA - MYC 465 1439 669 1193 779 RPS2 - MYC 800 611 554 1296 1246 RPS5 - MYC 1017 2057 792 2620 2634 RPS2P46 - MYC 1067 875 1081 1185 1097 RPS3A - MYC 1095 1202 1047 1179 1403 RPS18 - MYC 1249 1158 1412 2172 1838 RPS9 - MYC 1317 2441 1217 2634 5 RPS11 - MYC 1528 1874 1697 2256 1955 RPS27 - MYC 1573 2059 1750 2133 2311 RPS4X - MYC 1703 1167 1523 1797 2267 RPS3 - MYC 1757 1329 1903 1824 1960 RPS6KL1 - MYC 1763 2336 1411 2538 2624 RPS27A - MYC 1812 1000 1905 2186 1981 RPS14 - MYC 1886 1751 1836 2473 2404 RPS21 - MYC 1923 2238 1920 2671 2695 RPS24 - MYC 1938 1484 2104 2146 2354 RPS16 - MYC 1975 2296 1828 2284 2455 RPS19 - MYC 1995 2224 1947 2519 2249 RPS20 - MYC 2022 1118 1978 2116 2429 RPS13 - MYC 2048 1162 2230 2511 2590 RPS7 - MYC 2058 1001 2191 2465 12 RPS12 - MYC 2089 553 1898 2093 2101 RPS8 - MYC 2102 1470 1963 2155 2080 RPS29 - MYC 2112 1446 2080 2599 2600 RPS25 - MYC 2165 1255 2003 2152 2147 RPS23 - MYC 924 400 2056 1258 198 Table 11: 2nd order interaction ranking between MYC VS TRP members. 21 RANKING MYC TARGETS IN TRP MYC - X HSIC SOBOL laplace linear rbf 2002 jansen Ribosomal protein L RPL22 - MYC 621 799 1185 1201 1467 RPL5 - MYC 709 932 926 1426 1564 RPL7L1 - MYC 1204 1423 1519 2533 2559 RPL21 - MYC 1224 1263 1435 1737 124 RPL10A - MYC 1230 1512 1357 1958 1366 RPL24 - MYC 1285 1864 1032 2091 1996 RPL15 - MYC 1388 1478 1618 2471 2450 RPL19 - MYC 1434 1245 1135 2376 2388 RPL41 - MYC 1455 1240 1080 749 1648 RPL23 - MYC 1509 1524 1631 1556 1872 RPL13A - MYC 1513 1102 1481 2701 2741 RPL4 - MYC 1516 590 1657 1491 1301 RPL39 - MYC 1557 1818 1508 2311 2414 RPL30 - MYC 1567 1609 1690 2173 2169 RPL27A - MYC 1673 2090 1660 2568 2510 RPL35A - MYC 1682 1743 1632 2610 2628 RPL7 - MYC 1744 1364 1615 1784 1408 RPL7A - MYC 1770 1333 1891 2223 2059 RPL38 - MYC 1799 1835 1655 1940 2052 RPL26 - MYC 1841 2017 1862 2023 2077 RPL23A - MYC 1888 1168 1940 1814 54 RPL14 - MYC 1892 720 1975 2254 2386 RPL6 - MYC 1965 734 1766 1976 1794 RPL11 - MYC 1972 954 2049 2396 2572 RPL27 - MYC 1979 1945 2076 2354 1953 RPL12 - MYC 1990 1148 1984 2692 2705 RPL3 - MYC 2007 1497 1866 1561 1111 RPL37A - MYC 2033 1378 2083 2708 2738 RPL39L - MYC 2038 2079 2048 2625 2692 RPL37 - MYC 2061 2306 1982 2699 2675 RPL36A - MYC 2071 2038 1998 2185 1753 RPL31 - MYC 2194 1690 2355 2698 2743 RPL9 - MYC 2203 2001 2260 2713 2677 RPL35 - MYC 2222 2124 2275 2372 2152 RPL18A - MYC 2236 1814 2153 2220 108 RPL32 - MYC 2345 1538 2309 1942 2028 RPL34 - MYC 2538 1405 2478 2739 2719 RPL13 - MYC 2545 2168 2439 2399 2174 Table 12: 2nd order interaction ranking between MYC VS TRP members. 22 UNEXPLORED COMBINATORIAL HYPOTHESES TRP members synergy BEFORE drug treatment w.r.t EIF-2B3/2D MYC PABPC1L MYC RPS-A/2/2P46/3A/18/9/23 MYC RPL-22/5/7L1/21/10A/15/19/41/23/13A/4/3 MYC TRP members synergy AFTER drug treatment w.r.t EIF-3L/2B1/2AK4/4B/3E/3F/2B5/4EBP1 MYC PABPC4 MYC RPS-5/11/27/4X/3/6KL1/27A/14/21/24/16/... 19/20/13/7/12/8/29/25/23 MYC RPL-24/39/30/27A/35A/7/7A/38/26/23A/14/6/11/... 27/12/37A/39L/37/36A/31/9/35/18A/32/34/13 MYC Table 13: 2nd order combinatorial hypotheses between MYC and TRP members. 23 6.2.7. Vesicle protein / trafficking (VPT) Under this functional category in [16], the following MYC-target genes are reported - peroxisomal biogenesis factor (PEX3). Table 14 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 15 generated from analysis of the ranks in table 14. The table 14 shows rankings w.r.t MYC. For genes related to peroxisomal biogenesis factor (i.e PEX5 and PEX6); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to peroxisomal biogenesis factor (i.e PEX3); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the table 14 graphically, in table 15. RANKING MYC TARGETS IN VPT MYC - X HSIC SOBOL laplace linear rbf 2002 jansen peroxisomal biogenesis factor PEX5 - MYC 868 1142 856 1341 877 PEX3 - MYC 1527 2491 1845 2079 2004 PEX6 - MYC 2471 2463 2436 1456 1863 Table 14: 2nd order interaction ranking between MYC VS VPT members. UNEXPLORED COMBINATORIAL HYPOTHESES VPT members synergy BEFORE drug treatment w.r.t PEX-5/6 MYC VPT members synergy AFTER drug treatment w.r.t PEX3 MYC Table 15: 2nd order combinatorial hypotheses between MYC and VPT members. 24 6.2.8. Carbohydrate (CH) Under this functional category in [16], the following MYC-target genes are reported - aldehyde dehydrogenases (ALDH2); solute carrier family 2 facilitated glucose transporter (SLC2A4) and aldo-keto reductase family (AKR1A). Table 16 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 17 generated from analysis of the ranks in table 16. The table 16 shows rankings w.r.t MYC. For genes related to aldehyde dehydrogenases (i.e ALDH1B1, ALDH7A1 and ALDH5A1); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to aldehyde dehydrogenases (i.e ALDH3A2, ALDH9A1 and ALDH3A1); solute carrier family 2 facilitated glucose transporter (i.e SLC2A11) and aldo-keto reductase family (i.e AKR1C4); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the table 16 graphically, in table 17. RANKING MYC TARGETS IN CH MYC - X HSIC SOBOL laplace linear rbf 2002 jansen aldehyde dehydrogenases ALDH1B1 - MYC 340 522 516 89 544 ALDH7A1 - MYC 508 1070 205 310 806 ALDH5A1 - MYC 966 418 905 1014 1596 ALDH3A2 - MYC 1719 1872 1363 2076 2085 ALDH9A1 - MYC 1967 1727 1902 2227 2424 ALDH3A1 - MYC 2424 2165 2504 627 643 solute carrier family 2 ... facilitated glucose transporter SLC2A11 - MYC 2490 2499 2565 1324 1231 aldo-keto reductase family AKR1C4 - MYC 2447 2212 2343 1663 2021 Table 16: 2nd order interaction ranking between MYC VS CH members. 25 6.2.12. Signal transduction (STD) Under this functional category in [16], the following MYC-target genes are reported - cyclophilin peptidylprolyl isomerases (PPID); and phospholipases (PLA2G4A). Table 24 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 25 generated from analysis of the ranks in table 24. The table 24 shows rankings w.r.t MYC. For genes related to cyclophilin peptidylprolyl isomerases (i.e PPIA, PPID, PPIL1 and PPIH); and phospholipases (i.e PLCB4); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to cyclophilin peptidylprolyl isomerases (i.e PPIL3); and phospholipases (i.e PLA2G4A, PLA2G3, PLCG1, PLCB2 and PLCH1); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the table 24 graphically, in table 25. RANKING MYC TARGETS IN STD MYC - X HSIC SOBOL laplace linear rbf 2002 jansen cyclophilin peptidylprolyl isomerases PPIA - MYC 224 1857 546 530 1619 PPID - MYC 853 623 657 1291 1709 PPIL1 - MYC 905 1256 967 1279 1453 PPIH - MYC 949 602 460 813 1425 PPIL3 - MYC 2429 2142 2254 2600 2603 phospholipases PLA2G4A - MYC 2479 2501 2656 315 1243 PLA2G3 - MYC 2583 2408 2505 1178 1310 PLCG1 - MYC 1127 1768 987 1849 1729 PLCB2 - MYC 1766 2526 1927 721 703 PLCB4 - MYC 1859 477 1747 698 1306 PLCH1 - MYC 2677 2252 2538 2461 2417 Table 24: 2nd order interaction ranking between MYC VS STD members. 32 UNEXPLORED COMBINATORIAL HYPOTHESES STD members synergy BEFORE drug treatment w.r.t PPI-A/D/L1/H MYC PLCB4 MYC STD members synergy AFTER drug treatment w.r.t PLA-2G4A/2G3 MYC PLC-G1/B2/H1 MYC Table 25: 2nd order combinatorial hypotheses between MYC and STD members. 33 6.2.13. Nuclear regulatory factors (NRF) Under this functional category in [16], the following MYC-target genes are reported - canonical high mobility group (HMGN2); sirtuins (SIRT1); and OZF (ZNF146). Table 26 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 27 generated from analysis of the ranks in table 26. The table 26 shows rankings w.r.t MYC. For genes related to canonical high mobility group (i.e HMGN3, HMGB2, HMGB3 and HMGB1); and Sirtuins (i.e SIRT3); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to canonical high mobility group (i.e HMGN2 and HMGN5); and OZF (i.e ZNF146); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the table 26 graphically, in table 27. RANKING MYC TARGETS IN NRF MYC - X HSIC SOBOL laplace linear rbf 2002 jansen canonical high mobility group HMGN3 - MYC 183 79 363 193 367 HMGB2 - MYC 317 819 92 150 275 HMGB3 - MYC 457 1224 774 829 624 HMGB1 - MYC 1318 1348 1093 1618 1849 HMGN2 - MYC 1604 345 1627 874 1876 HMGN5 - MYC 2532 2729 2490 338 1429 Sirtuins SIRT3 - MYC 333 1732 127 1290 2044 OZF ZNF146 - MYC 883 1908 1313 2246 2378 Table 26: 2nd order interaction ranking between MYC VS NRF members. 34 UNEXPLORED COMBINATORIAL HYPOTHESES NRF members synergy BEFORE drug treatment w.r.t HMG-N3/B2/B3/B1 MYC SIRT3 MYC NRF members synergy AFTER drug treatment w.r.t HMG-N2/N5 MYC ZNF146 MYC Table 27: 2nd order combinatorial hypotheses between MYC and NRF members. 35 6.2.14. Nucleolus / RNA-binding protein (NRBP) Under this functional category in [16], the following MYC-target genes are reported - dyskerin pseudouridine synthase 1 (DKC1); stem-loop histone mRNA binding protein (SLBP); nucleolin (NCL); surfeit (SURF6); nucleolar protein (NOL1); heterogeneous nuclear ribonucleoprotein (HNRPA1 and HNRNPA2B1); and RNA binding motif protein (RBM3). Table 28 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 29 generated from analysis of the ranks in table 28. The table 28 shows rankings w.r.t MYC. For genes related to dyskerin pseudouridine synthase 1 (i.e DKC1); nucleolin (i.e NCL); nucleolar protein (i.e NOL8 and NOL6); heterogeneous nuclear ribonucleoprotein (i.e HNRNPA3, HNRNPA1L2, HNRNPA1 and HNRNPD); and RNA binding motif protein (i.e RBMX); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to stem-loop histone mRNA binding protein (i.e SLBP); surfeit (i.e SURF6); nucleolar protein (i.e NOL10, NOL9 and NOL11); heterogeneous nuclear ribonucleoprotein (i.e HNRNPM, HNRNPC, HNRNPA0, HNRNPH3 and HNRNPR); and RNA binding motif protein (i.e RBM19, RBM28 and RBM26); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the table 28 graphically, in table 29. 36 RANKING MYC TARGETS IN NRBP MYC - X HSIC SOBOL laplace linear rbf 2002 jansen dyskerin pseudouridine synthase 1 DKC1 - MYC 511 233 359 669 1601 stem-loop histone mRNA binding protein SLBP - MYC 2182 1734 2266 2195 31 nucleolin NCL - MYC 774 1120 1011 1122 1059 surfeit SURF6 - MYC 2073 474 1950 1796 2369 nucleolar protein NOL8 - MYC 1187 331 1398 1158 2072 NOL6 - MYC 1331 914 1293 947 786 NOL10 - MYC 1590 1626 1565 1952 1772 NOL9 - MYC 1896 2263 1923 2335 2240 NOL11 - MYC 2004 1456 1759 1859 1457 heterogeneous nuclear ribonucleoprotein HNRNPA3 - MYC 48 1299 189 1203 1373 HNRNPM - MYC 1050 2431 1043 1747 2100 HNRNPA1L2 - MYC 1072 393 846 734 244 HNRNPA1 - MYC 1083 1401 1334 1921 1461 HNRNPD - MYC 1140 1697 1419 1087 1947 HNRNPC - MYC 1236 1545 1441 2262 2315 HNRNPA0 - MYC 1838 533 1781 2712 2733 HNRNPH3 - MYC 2313 2497 2352 1226 1647 HNRNPR - MYC 2496 2177 2515 2724 2702 RNA binding motif protein RBMX - MYC 1460 240 1133 1681 397 RBM19 - MYC 1655 2350 1684 2687 2727 RBM28 - MYC 1898 993 1732 1599 2161 RBM26 - MYC 1918 1891 1737 2458 2314 Table 28: 2nd order interaction ranking between MYC VS NRBP members. 37 UNEXPLORED COMBINATORIAL HYPOTHESES NRBP members synergy BEFORE drug treatment w.r.t DKC1 MYC NCL MYC NOL-8/6 MYC HNRNP-A3/A1L2/A1/D MYC RBMX MYC NRBP members synergy AFTER drug treatment w.r.t SLBP MYC SURF6 MYC NOL-10/9/11 MYC HNRNP-M/C/A0/H3/R MYC RBM-19/28/26 MYC Table 29: 2nd order combinatorial hypotheses between MYC and NRBP members. 38 6.2.15. Transcription factors (TF) Under this functional category in [16], the following MYC-target genes are reported - achaete-scute family bHLH transcription factor 2 (ASCL2); ETS proto-oncogene 2, transcription factor (ETS2); MYB proto-oncogene (MYBL2); E2F transcription factor (E2F1); transcription factor (TCF12); HOXL subclass homeoboxes (HOXD13); NME/NM23 nucleoside diphosphate kinase (NME1); and forkhead boxes (FOXM1). Table 30 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 31 generated from analysis of the ranks in table 30. The table 30 shows rankings w.r.t MYC. For genes related to achaete-scute family bHLH transcription factor 2 (i.e ASCL2); ETS proto-oncogene 2, transcription factor (i.e ETS2); MYB proto-oncogene (i.e MYBL2, MYB and MYBL1); E2F transcription factor (i.e E2F2, E2F7, E2F1 and E2F8); transcription factor (i.e TCF19 and TCF7); HOXL subclass homeoboxes (i.e HOXB9, HOXB8, HOXB5 and HOXA9); NME/NM23 nucleoside diphosphate kinase (i.e NME1); and forkhead boxes (i.e FOXM1); all show majority low rankings (below half way mark) across the SA methods. That is, the low rankings indicate that a combination is weakly down regulated after ETC1922159 treatment. Thus, they might be working synergistically in up regulated manner in CRC BEFORE the drug treatment. On the other hand, genes related to E2F transcription factor (i.e E2F5); transcription factor (i.e TCFL5 and TCF3); HOXL subclass homeoboxes (i.e HOXB7, HOXB3, HOXB13, HOXA11 and HOXB4); NME/NM23 nucleoside diphosphate kinase (i.e NME4); and forkhead boxes (i.e FOXA2 and FOXJ1); all show majority high rankings (above half way mark). That is, the high rankings indicate that a combination is strongly down regulated after ETC-1922159 treatment. Thus, they might be working synergistically in down regulated manner in CRC AFTER the drug treatment. One can also interpret the results of the table 30 graphically, in table 31. 39 RANKING MYC TARGETS IN TF MYC - X HSIC SOBOL laplace linear rbf 2002 jansen achaete-scute family bHLH transcription factor 2 ASCL2 - MYC 153 356 280 1 476 ETS proto-oncogene 2, transcription factor ETS2 - MYC 1118 1412 1415 1020 1614 MYB proto-oncogene MYBL2 - MYC 59 26 6 121 545 MYB - MYC 261 638 529 261 1105 MYBL1 - MYC 484 177 249 462 973 E2F transcription factor E2F2 - MYC 94 188 76 630 1196 E2F7 - MYC 232 479 795 400 842 E2F1 - MYC 316 1461 294 645 1002 E2F8 - MYC 444 344 241 214 588 E2F5 - MYC 2050 2084 1600 2584 2473 transcription factor TCF19 - MYC 1051 1363 1585 2159 2257 TCF7 - MYC 1139 1516 1406 1007 1181 TCFL5 - MYC 2141 1205 2245 1993 1511 TCF3 - MYC 2562 2269 2463 2601 2605 HOXL subclass homeoboxes HOXB7 - MYC 284 2116 456 1816 2122 HOXB9 - MYC 295 306 321 533 338 HOXB8 - MYC 918 462 767 472 447 HOXB5 - MYC 937 1220 645 1665 791 HOXA9 - MYC 1387 1054 1326 1677 1316 HOXB3 - MYC 1554 2454 1128 2736 2726 HOXB13 - MYC 2093 1812 2002 2680 2564 HOXA11 - MYC 2224 1386 2069 2021 2281 HOXB4 - MYC 2727 2671 2728 1504 946 NME/NM23 nucleoside diphosphate kinase NME1 - MYC 911 351 896 385 672 NME4 - MYC 2287 2386 2181 2233 2110 forkhead boxes FOXM1 - MYC 127 65 262 68 499 FOXA2 - MYC 1064 1644 864 2164 1813 FOXJ1 - MYC 2533 2736 2610 324 953 Table 30: 2nd order interaction ranking between MYC VS TF members. 40 UNEXPLORED COMBINATORIAL HYPOTHESES TF members synergy BEFORE drug treatment w.r.t ASCL2 MYC ETS2 MYC MYB, MYBL-1/2 MYC HNRNP-A3/A1L2/A1/D MYC E2F-2/7/1/8 MYC TCF-19/7 MYC HOX-B9/B8/B5/A9 MYC NME1 MYC FOXM1 MYC TF members synergy AFTER drug treatment w.r.t E2F5 MYC TCF-L5/3 MYC HOX-B7/B3/B13/A11/B4 MYC NME4 MYC FOX-A2/J1 MYC Table 31: 2nd order combinatorial hypotheses between MYC and TF members. 41 9. Author’s contributions SS designed, developed and implemented the insilico experimental setup, wrote the code, generated and analysed the results and wrote the manuscript. 10. Acknowledgements Special thanks to Mrs. Rita Sinha and late Mr. Prabhat Sinha for supporting the author financially, without which this work could not have been made possible. 11. Code Availability Code of the search engine used to generate the rankings has been made available on CERN based Zenodo at https://zenodo.org/records/14636112. 12. Source of Data Data used in this research work has been released online publicaly, in a publication in [2]. This data was made available in the form of supplementary table. Related to this data, on NCBI Gene Expression Omnibus (GEO) Series GSE69687 https: //www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE69687, click on Download RNA-seq counts button, it opens a page that contains Human gene annotation table (at the bottom of the page). The file Human.GRCh38.p13.annot.tsv.gz contains the range of genes with ENSEMBLGENEID all starting with ENSG. This ENSG identifier is used to index the recording of the regulated genes, in the data made available in the supplementary table in [2]. The data itself is available as supplementary material in the journal, however, the indexing of the genes used in the supplementary material is available on NCBI NIH database. 13. References References [1] S. Sinha, Machine learning ranking of plausible (un)explored synergistic gene combinations using sensitivity indices of time series measurements of wnt signaling pathway, Integrative Biology (2024). [2] B. Madan, Z. Ke, N. Harmston, S. Y. Ho, A. Frois, J. Alam, D. A. Jeyaraj, V. Pendharkar, K. Ghosh, I. H. Virshup, et al., Wnt addiction of genetically defined cancers reversed by porcn inhibition, Oncogene 35 (2016) 2197. [3] S. 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