Patent Application
20240392383
In previous patent applications (e.g. WO2019/012149A1, which published in the USA as US20200306253A1; a later filing of mine, with more content, is AU2019208238A1; all these filings are herein incorporated in their entirety by reference; if anything is unclear or ambiguous in the present text, [for non-limiting example if a term is undefined], guidance can be found in one or more of these published applications) I have reported how almitrine dimesylate has greater anti-cancer activity than many present cancer drugs (e.g. carboplatin, one of the most used) in standardized pre-clinical NCI-60 testing at the National Cancer Institute (USA). Yet almitrine dimesylate has less, close to no, side-effects in humans. Greater anti-cancer activity, less side-effects. This combination meets an unmet medical need.
Many cancer drugs cause horrific side-effects. Lethal in some cases. Nearly universally harming quality of life, without necessarily extending it by much. By contrast, almitrine dimesylate, which has been taken for decades in humans, and for millions of patient months [1], has no side-effects in nearly every patient that takes it daily for <11 months. And only causes side-effects in a very small minority of the many patients that take it daily for >11 months to years [2]. Wherein these side-effects, if they do occur, are relatively minor, and distinctly reversible (reversible upon stopping the course in nearly all cases [2]), compared to present cancer drugs (e.g. refer chemotherapy induced peripheral neuropathy, CIPN, often permanent). So, almitrine (e.g. almitrine dimesylate) can be prescribed to patients more freely than a typical cancer drug, because it is not likely to harm, especially when used short-term, and/or in cycles, as most cancer drugs are.
Almitrine is especially useful, meeting immense unmet medical need, for the significant proportion of patients that withdraw consent to cancer treatment because of side-effects. Either because they are absolutely intolerable for the patient. Or the patient doesn't consider them worth the predicted increase in life expectancy, which can be short. Moreover, almitrine is useful in cases that the cancer patient is elderly/frail, for (non-limiting) example over 60 years old, wherein such patients often can't physically withstand one or more of chemo-, radio-, immuno-therapy, surgery/transplant etc.
Because present cancer drugs are so harmful, there is a lot of research interest in trying to discover cancer biomarkers, which can predict if a cancer will respond to a particular cancer drug. With the aim that each cancer drug is only administered to cancer patients who stand a reasonable chance of being able to benefit from it. Because almitrine isn't typically harmful, the biomarker imperative isn't as much, but there is still much utility for biomarkers that can predict which cancers will best respond to almitrine treatment. For example, biomarkers can assist clinical trialling of almitrine vs. cancer. Wherein biomarker(s), predicting the cancers most susceptible to the anti-cancer activity of almitrine, enable those cancer patients likely to benefit most to be admitted to a clinical trial(s). Conferring a stronger clinical signal to the FDA/EMA or equivalent. Herein biomarkers, methods and kits are disclosed that can predict a cancer's susceptibility to the anti-cancer activity of almitrine (e.g. almitrine in the form of almitrine dimesylate). Excitingly, cancer susceptibility to almitrine dimesylate correlates with biomarkers that correlate with, and drive, resistance to many present cancer drugs, and poor prognosis. Thence, almitrine disproportionally targets at least some of the most dangerous cancers.
Componentry to this disclosure is each method I used to find the biomarkers of cancer susceptibility/resistance to almitrine, as presented herein. Moreover, any one or more of these methods applied to a different compound/drug, or plurality thereof. For non-limiting example, a compound/drug that has undergone NCI-60 testing at the National Cancer Institute (NCI), and/or the same/similar method of testing elsewhere. For non-limiting example, a drug approved for at least one clinical use, optionally/preferably for anti-cancer use, by one or more of the Food and Drug Administration (FDA) in the USA, the European Medicines Agency (EMA), the Medicines and Healthcare products Regulatory Agency (MHRA) in the UK, the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan, the National Medical Products Administration (NMPA) in China, and/or one or more similar regulatory authorities in another country/jurisdiction (preferably, but not restrictively, which has been tested in NCI-60 testing). For non-limiting example, a compound/drug in clinical trials for cancer, or a compound/drug that has failed one or more clinical trials for cancer. And the biomarker(s) found by one or more of the methods herein are componentry to this present disclosure. Also componentry to this disclosure are method variants/truncations/extensions/equivalents, which will be apparent to one of the art, now they have this disclosure in hand. And the biomarker(s) found by such a method equivalent(s)/variant(s).
A componentry method of this disclosure is to enter the CAS number (or name) of an approved/licensed/candidate/failed candidate cancer drug (e.g. approved by the FDA and/or EMA, and/or equivalent/similar regulatory body in another country/jurisdiction) into the text box of https://dtp.cancer.gov/dtpstandard/dwindex/index.jsp (making the appropriate radio button selections, as clear to one of the art). This database is hereby incorporated in its entirety by reference; indeed, all databases referred to herein are incorporated herein in their entirety by reference. Then, if the drug is present in this database, noting its “NSC number”, findable in its database entry. Then using this NSC number with NCI COMPARE, available at http://dtp.cancer.gov/public_compare/ (or, which might be found in the future at, https://nci60.cancer.gov/publiccompare/), introduced at https://dtp.cancer.gov/databases_tools/compare.htm, and selecting its settings, and analysing its output, optionally corroborating (e.g. by using one or more of the corroborating methods herein, e.g. by using the CellMiner database [3-4]), by at least one method herein, and/or by a equivalent(s)/variant(s) thereof, to retrieve a biomarker(s) for cancer susceptibility/resistance to that cancer drug. This biomarker(s) can then be used in a further method(s) herein, and/or in a method(s) of the art for using a biomarker(s). As illustrated herein, taught by way of example, for almitrine dimesylate.
This method can be performed for a number of approved/licensed/candidate/failed candidate cancer drugs, optionally by selecting one or more cancer drugs from a publicly available list(s) of cancer drugs (e.g. as found by using the Google search engine, non-limiting e.g. https://www.cancer.gov/about-cancer/treatment/drugs, https://www.cancerresearchuk.org/about-cancer/cancer-in-general/treatment/cancer/cancer-drugs/drugs, https://en.wikipedia.org/wiki/List_of_chemotherapeutic_agents, https://en.wikipedia.org/wiki/List_of_antineoplastic_agents) and/or, in a preferred embodiment, listed in the paper (and/or in one or more of its supplementary tables): Holbeck S L, Collins J M, Doroshow J H (2010) Analysis of Food and Drug Administration-approved anticancer agents in the NCI60 panel of human tumor cell lines. Molecular cancer therapeutics. 9(5):1451-60.
A componentry method of this disclosure is to enter the name or, more preferably, the NSC number (NSC numbers are presented in brackets after each cancer drug name) of one of the following cancer drugs into the appropriate text box of https://dtp.cancer.gov/public_compare/, and subsequently using a method(s) herein (e.g. as illustrated, teaching by way of example, for almitrine dimesylate), and/or an equivalent/variant(s) thereof, to retrieve a biomarker(s) for cancer susceptibility/resistance to that cancer drug, wherein in some embodiments this is done for a plurality of these cancer drugs, and in some embodiments it is done for all of these cancer drugs:
Herein, by disclosing the database (herein incorporated in its entirety by reference, especially the database entries of the aforementioned NSC numbers), the cancer drug(s), and a method(s) to retrieve a biomarker(s) for cancer susceptibility/resistance (to each specified cancer drug) from the database, the biomarker(s) has been disclosed herein (incorporated herein by reference). In a very efficient, concise manner. This biomarker(s) can then be used in a further method(s) herein, and/or in a method(s) of the art for using a biomarker(s).
With the method(s) disclosed herein in hand, retrieving such a biomarker(s) is routine. By disclosing the method(s), where and how to apply the method(s), I have disclosed the biomarker(s). The number of cancer drugs listed here is relatively small, and one of the art can readily use the teaching of this disclosure to retrieve zero or more biomarkers for each of these named/specified cancer drugs, meaning any such biomarker(s) is in turn componentry to this disclosure, as is its use in a method(s) herein, and/or in a method(s) of the art for using a biomarker(s).
If any of the above internet links become non-functional, then the new, updated links or equivalents can be found by contacting the National Cancer Institute (NCI, USA), especially its Developmental Therapeutics Program (DTP) section. This contact can include going to the NCI to enquire. If there is any difficulty in navigating the internet pages at the NCI, again guidance is available from the NCI team, e.g. from Dr. Mark W. Kunkel there (Email: kunkelm@mail.nih.gov). Note that a symbol/icon used for COMPARE, at some points on the NCI website, is a circle with a capital C inside of it. Downloading, for offline use, any database mentioned herein is contemplated. If for some reason a method herein isn't possible for one drug mentioned herein, try with a different drug(s) herein.
If/when a cancer biomarker(s) that is already known to apply for the drug (it is part of the state of the art) is found by this method, in some embodiments it is dismissed. Preferably substituted by a biomarker(s) that is not part of the state of the art.
Particularly contemplated is using a biomarker(s), found by a method(s) herein, to select a cancer patient(s) for a candidate cancer drug trial. And/or to (optionally retrospectively) analyse a candidate cancer drug trial (possibly assessing whether the drug was more active for any sub-population(s) of subjects [their cancers], as identified by one or more biomarkers). And/or to select which cancer drug(s) to administer to a subject with cancer. In some embodiments, if a subject's cancer is predicted to be susceptible/responsive to a compound(s)/drug(s), by one or more biomarkers acquired by a method(s) herein, then a therapeutically effective amount of this compound(s)/drug(s) is administered to the subject. In some embodiments, if a subject's cancer is predicted to be resistant to a compound(s)/drug(s), by one or more biomarkers acquired by a method(s) herein, then this compound(s)/drug(s) is not administered to the subject, optionally wherein a different compound(s)/drug(s) is administered to the subject instead, optionally a compound(s)/drug(s) that the subject's cancer is predicted to be susceptible/responsive to by one or more biomarkers acquired by a method(s) herein.
A preferred cancer drug(s) to retrieve a biomarker(s) for and use, using a method(s) of this disclosure, is a cancer drug with a large number of USD $sales in the preceding year. For example, of the order of USD $billions. For non-limiting example, one or more of (NSC numbers in brackets) Lenalidomide (747972), Ibrutinib (761910), Palbociclib (758247), Enzalutamide (766085; 755605), Osimertinib (779217). Note that Biologics (e.g. antibody based drugs, e.g. checkpoint inhibitors thereof) are contemplated. Including immunotherapies. Including (without limitation) Nivolumab (Opdivo), Pembrolizumab (Keytruda), Ipilimumab (Yervoy), Atezolizumab, Avelumab, Durvalumab.
A preferred drug(s) to retrieve a biomarker(s) for and use, using a method(s) of this disclosure, is a candidate cancer drug (or failed candidate thereof) developed/owned by a multinational (and/or publically listed) pharmaceutical company. Optionally one of those listed in a list of 100 (or 20, or 10) pharmaceutical companies with the greatest USD$ annual sales in the preceding year. For example, Servier.
In clinical use, almitrine is often administered as almitrine dimesylate. But the teaching herein is not restricted to that form, and other salts of almitrine, and the administration of almitrine without a salt, are also contemplated. Indeed, without restriction, any clinically used form of almitrine (and/or form disclosed in WO2019/012149A1) is contemplated. Herein a reference to almitrine, or a salt thereof, is, in alternative embodiments, a reference to a different almitrine containing composition, preferably a pharmaceutical composition thereof. Some prototypical, non-limiting, almitrine containing compositions are specified in WO2019/012149A1.
If a term/component is undefined or unclear/ambiguous herein, please refer to WO2019/012149A1 or AU2019208238A1 for clarification/specification, which are both herein incorporated in their entirety by reference. For example, the bounds of a “subject” is specified therein. Routes of administration, some (without restriction) contemplated cancer types etc. are therein and thence are herein by incorporation by reference. In some embodiments, “effective amount” herein is substituted with “therapeutically effective amount”. Where a mean or average is referred to herein, this can be the arithmetic mean, or (in alternative embodiments) some other mean (e.g. logarithmic, e.g. harmonic), or be the median instead. At points herein that a Pearson correlation coefficient is referred to, or used, in alternative embodiments a different correlation measure of the art is used, e.g. Spearman's rank correlation coefficient. If there is any contradiction of definition(s) in this disclosure, all the given definitions are valid but for different embodiments of the disclosure.
A single gene typically produces multiple transcripts. In humans, the average is 15.2 different transcripts per gene [5]. The method described in the section hereafter operates at the gene level, by assuming that different transcript sequences from a single gene correlate, or don't correlate, similarly to a drug(s) susceptibility. But this assumption can be misplaced. So, also componentry to this disclosure is to operate at the transcript rather than the gene level: to consider different sequence transcripts from the same gene independently, incorporating the possibility that a sequence transcript(s) of a gene can correlate with a drug(s) susceptibility/resistance, whilst a different sequence transcript(s) from the same gene doesn't, or doesn't to the same degree. Herein, wherever gene expression is referred to, in alternative embodiments, a transcript sequence(s) expression, independently of a different sequence transcript(s) from the same gene, is being referred to instead. So, to predict a cancer's susceptibility to a drug(s), instead of looking at the gene expression level, wherein any one or more of a gene's different sequence transcripts is considered, possibly wherein their expression is averaged, the expression of one or more specific sequence transcripts from a gene(s) is considered, the expression of which correlates with drug(s) susceptibility or resistance.
Distinct from above, for almitrine dimesylate I used a different web link to access NCI COMPARE: https://dtp.cancer.gov/private_compare/login.xhtml. Where this link is only for those with a private account. Because, distinctly, the NCI-60 data for almitrine dimesylate is not yet public at the NCI. And is only accessible via my private, password protected account.
GI50 is compound concentration that causes 50% growth inhibition of a cell line relative to no-drug control. Cancer gene expression correlations to almitrine dimesylate susceptibility were found using [NCI 5-dose almitrine dimesylate GI50 anti-cancer data] with the NCI COMPARE algorithm [6-7]. NCI COMPARE outputs the Pearson correlation coefficient (R) between each gene expression and −log 10(GI50). So, NOT GI50! Thence, a greater positive correlation=higher gene expression=lower GI50, i.e. greater anti-cancer drug potency. Six independent gene expression data sets were searched, wherein these were from a most favoured/trusted collection at the NCI, called “MOLTID_GC_SERIES_MICROARRAY_ALL” in the NCI COMPARE system. This delimited collection excludes the other gene expression data sets available in NCI COMPARE that aren't determined as equivalently/sufficiently reliable by the NCI staff. This concurrent searching in six independent trusted gene expression data sets enables corroboration; is a correlation observed with same sign, and at notable magnitude, in more than one trusted gene expression data set? Moreover, because some genes can produce more than one transcript sequence [5], each gene expression data set can include more than one transcript sequence for each gene. Indeed, these gene expression data sets are better thought of as transcript expression data sets, because a single gene, in many—but not all—cases, can produce multiple different sequence transcripts. Wherein the expression of each can be independently (or non-independently, depending on probe design) reported, assuming each was known to exist (or predicted to exist) at the time of the microarray chip design, and a probe for each was selected for incorporation on the constrained space of the chip. In cases that multiple different sequence transcripts/probes from a single gene correlate with susceptibility to almitrine dimesylate, this is stronger affirmation that this gene expression correlates with susceptibility/resistance to almitrine dimesylate. Indeed, the more times that a gene expression is observed to appreciably correlate, with same sign, with susceptibility to almitrine dimesylate, within a single gene expression data set, and/or across different gene expression data sets, the more conviction we can have that the correlation is valid. Componentry to this disclosure is to rank/order/select gene expression correlation(s) to a drug(s) susceptibility/resistance, for reliability/robustness/conviction, by the number of times that each correlation is observed above a selected threshold (e.g. Pearson) correlation coefficient value (|R|). Using this ranking method, potentially more conviction can be drawn for genes with a greater number of different sequence transcripts/probes in the gene expression database(s) used, biasing towards these. This bias can be removed by dividing each gene value (the number of times that a correlation, above a selected value of |R|, is observed for that gene), by the number of probes for that gene. Alternatively, this bias can be removed by only considering a single transcript/probe sequence per gene, per gene expression database, preferably wherein the transcript/probe sequence with the highest correlation to the drug(s) susceptibility/resistance is selected. Another source of bias can come if not all the same genes are necessarily componentry to all the gene expression databases (e.g. 2 of the 6 gene expression databases in the selected NCI gene expression database set are much smaller than the other 4, as shown in a table later): a given gene may be present in one or more of the gene expression databases searched, wherein a strong correlation to a drug sensitivity might be detected, but absent from another gene expression database(s), and so using this ranking method by number of occurrences observed above correlation threshold, the relative importance of this gene expression correlation, as compared to other genes that are present in all the gene expression databases considered, might potentially be underestimated. However, although this method, uncontrolled, might conceivably, for some use scenarios, underestimate the relative importance of some gene expression correlations as compared to others, the most highly ranked gene expression correlations by this method, especially if a high threshold correlation value is selected, are likely to be absolutely robust and reliable. This is very valuable.
In the table below, the six gene expression data sets that were used within NCI COMPARE, are each allocated a serial code that will be used to refer to each hereafter, with their gene numbers estimated, enabling a multiple comparisons correction for each:
Correlations first found using NCI COMPARE, with the aforementioned method, were then further corroborated using cancer gene expression data sourced from the CellMiner database [3-4]. Indeed, the following NCI 1-dose and 5-dose anti-cancer data for almitrine dimesylate, with serial codes given that will be used to refer to each hereafter, was compared with cancer gene expression data sourced from CellMiner:
The CellMiner database includes five independent Human Genome gene expression data sets {accession numbers: GSE5949, GSE5720, GSE32474, GSE29682, GSE29288}, each of which can have multiple different transcript sequence probes for a single gene, wherein the mean expression for each gene was taken: within and across the different databases, which is distinct from NCI COMPARE where all expressions of different sequence transcripts for a gene were kept distinct without any averaging, and where there was no averaging across gene expression data sets, each was kept distinct. Although actually only four of these gene expression data sets in CellMiner were used to generate the mean for each gene expression: the single gene expression data set that used an Agilent instead of an Affymetrix microarray chip (GSE29288) was excluded from the mean because, to quote the CellMiner documentation: “Agilent probe(s) values are removed from cell line average intensity calculation due to normalization inconsistency with Affymetrix probesets”. Raw (log 2), rather than z scored, gene expression data was used (using z scored data would have permitted the Agilent probe data to be used also, since according to CellMiner documentation, z scoring makes Affymetrix and Agilent probe data interoperable, but raw data was favoured anyhow. Some exploratory testing using the z scored, Agilent incorporating, data instead of raw data showed essentially equivalent results for the data fraction looked at). So, the data set sourced from an Agilent microarray chip was not incorporated in the mean, but was instead used by itself separately for an independent verification. With much less data, the Agilent set is much less reliable than the mean of the 4 Affymetrix microarray data sets, but it is a nice independent adjudicator. This Agilent data set is not one of the six gene expression data sets at NCI COMPARE. By contrast, there is some overlap in the 4 CellMiner gene expression databases that were used for the mean and the 6 that were used with NCI COMPARE (some might be the same). However, at least one of these data sets is not found in NCI COMPARE (its accession number: GSE29682). Also, distinctly with the 4 Affymetrix microarray data sets in CellMiner, the mean expression for each gene was taken: averaging the expression of all the different sequence transcripts, across all the 4 used Affymetrix microarray data sets, for each gene. Moreover, this mean is very heavily weighted to a database that CellMiner has, and NCI COMPARE definitely doesn't, Accession number: GSE29682, because it tends to have many more probes for each gene than any other data set, indeed often more than all the others combined, each of which is an additional data point in the mean for each gene. This is because the data set with Accession number, GSE29682, was created using the Affymetrix Human Exon 1.0 ST Array, which has 4 probes per exon on average, and thence the number of probes for any given gene tends to scale with the number of exons it has, where the average gene has 10 exons and so there are, on average, 40 probes per gene on this chip. Thus, there is definite margin for the gene expression correlations drawn from CellMiner to diverge from NCI COMPARE, and thence their non-divergence, and agreement, is a strong, valuable confirmatory signal. Given its inclusion of GSE29682, which is the most recent and advanced gene expression data set for the NCI-60 cancer cell lines, sourced by utilising the most recent and advanced microarray chip, the Affymetrix Human Exon 1.0 ST Array, the CellMiner data is to be particularly prized. An optional additional step could conceivably make it marginally better, but this wasn't implemented (“juice not worth the squeeze”—would make no tangible difference in most cases), wherein, across the NCI-60 cancer cell lines, if an exon probe(s) expression in CellMiner doesn't appreciably correlate (Pearson, e.g. |R|>0.3) with the majority of the other exon probes for that same gene in CellMiner, it is cut from consideration and isn't used in the calculation of the mean expression for that gene.
To make a multiple comparisons correction the gene number in the gene expression database needs to be known. There are 25,722 transcripts (including genes, pseudogenes, and open reading frames) in the CellMiner database [8], wherein this transcript number can be used in lieu of gene number, which overcorrects.
Bonferroni corrected p-value threshold=0.05/25,722=0.00000194386=0.000002.
Šidák corrected p-value threshold=1−(1−0.05){circumflex over ( )}(1/25,722)=0.00000199413=0.000002.
These are both overly conservative Multiple Comparisons corrections because a non-gene would never be selected, yet non-genes (e.g. pseudogenes) are included in the correction.
Furthermore, Bonferroni (or Šidák) is a very conservative correction method, erring to exclude false positives at the risk of introducing false negatives i.e. potentially missing true, rather than possibly reporting false, correlations.
Bonferroni corrected p-value threshold for 2 genes in combination=0.05/(25,722*2)=0.000000971930643=0.000001.
Bonferroni corrected p-value threshold for 9 genes in combination=0.05/(25,722*9)=0.000000215984587=0.0000002.
An alternative/supplement to a Multiple Comparisons correction, as utilized by this work, is to observe if a sizeable correlation (with same sign, + or −) is observed in independently sourced data sets: the more, the greater likelihood it is a true correlation.
To recap so far, correlations first found using NCI COMPARE, with the aforementioned method, were then externally corroborated using cancer gene expression data, produced by microarray chip technology, sourced from the CellMiner database [3-4]. To disclose the next step now: these correlations were then further corroborated by using RNA-seq gene expression data from the CellMiner database [9]. Such corroboration is partially hampered because the correlation between microarray and RNA-seq sourced gene expression data is not complete in CellMiner: Pearson correlation coefficient=0.64 [9]. However, good concordance was observed herein, generally. In some embodiments, if a subject's cancer's RNA-seq gene expression data is going to be a/the method used to select a subject(s) for almitrine dimesylate treatment, a gene(s) expression(s) that correlates in RNA-seq data to almitrine dimesylate susceptibility should be employed for (de-)selection. Gene isoforms are mRNAs that are produced from the same DNA locus by different transcription start and/or stop sites, splicing variation etc. The RNA-seq method permits individual reporting upon the expression of different isoforms of a gene(s) [9]. Herein, presenting as in CellMiner, isoform gene expressions are reported as log 2(FPKM+1), where FPKM is “Fragments Per Kilobase per Million reads” and the “composite” gene expression of all the isoforms of a gene, presenting as defined in CellMiner and as used herein, is, to illustrate by example, for IsoformX and IsoformY: log 2(1+((2{circumflex over ( )}(IsoformX)−1)+(2{circumflex over ( )}(IsoformY)−1))), where IsoformX=log 2(FPKM+1) and IsoformY=log 2(FPKM+1).
Cancer gene expression correlations to almitrine susceptibility were first found using:
In alternative embodiments of the disclosure, this method, and/or component(s)/variant(s)/derivative(s) thereof, is utilized with a different drug(s). Optionally a candidate (or failed candidate) and/or approved (e.g. FDA/EMA/MHRA/PMDA/NMPA approved) anti-cancer drug(s), optionally one or more of the named/specified cancer drugs herein.
Assaying the gene expression of a cancer to predict its susceptibility to almitrine dimesylate treatment: the most predictive power, for least effort/complexity, is acquired by using [SCAF11-RAPH1], wherein a greater product predicts greater cancer susceptibility. Basis shown in
This Results section presents results that build up to the most robust/favoured correlations, which are disclosed late in this section under the “RNA-seq data” heading, which presents RNA-seq alongside microarray data. And, in turn, the most favoured of those correlations are disclosed in
Some selected Affymetrix microarray chip sourced results:
A comparator from other's work, giving perspective upon what a good correlation is: PTGR1 (alkenal/one oxidoreductase, AOR), which has been experimentally shown to bioactivate irofulven (hydroxymethylacylfulvene, HMF) into a potent anti-cancer form [10-11]: in NCI COMPARE, using NCI 5-dose testing data, PTGR1 expression correlates with susceptibility to the anti-cancer activity of irofulven: five correlations observed at >0.4, one of which is >0.5 at 0.523. To contrast, by example, in NCI COMPARE: SCAF11 expression correlation to anti-cancer activity of almitrine dimesylate: eight correlations observed at >0.4, one of which is >0.5 at 0.542.
In the gene lists below, following each gene name, in brackets, is how many times in NCI COMPARE's six independent gene expression data sets (some having multiple probes for some genes, typically because these genes produce more than one transcript sequence) that the gene expression correlation to almitrine dimesylate susceptibility is observed at: |R|>0.5, >0.4, >0.3 respectively. In each gene case below, the correlation is in the same direction (positive or negative) for all the observations. Greater faith can be entrusted in a correlation the more times it is observed, with same directionality. For some gene expressions, the number of observations of correlation to almitrine dimesylate susceptibility is large e.g. SCAF11. The ordering of the genes, as shown, ranks by number of correlation observations, with some intersecting consideration of amplitude of observed correlations also. Ranking (completely by, or in part by) gene expression correlations to a drug(s) activity: by number of times that this correlation is observed (with same directionality) is componentry to this disclosure, wherein, in a further embodiment(s), this ranking is used to identify which gene expression(s) to utilize in predicting a drug(s) activity, wherein, in a further embodiment(s), a cutoff is used, above which—above a number of observed times (above a specified |R| cutoff e.g. |R|>0.3)—gene expression correlations are included in the drug(s) activity prediction model, and at or below which they are excluded. In a componentry embodiment(s), when not all the observed correlations (e.g. above the cutoff e.g. |R|>0.3) for a gene expression are in the same direction (positive or negative) this gene is excluded from the prediction model. In a non-limiting embodiment(s) the cutoff is set at: observed at, or more than, two times (at |R|>0.3). The cutoff can be set higher for greater reliability (either by increasing the observed number requirement and/or increasing x in the |R|>x stipulation) and a smaller number of genes in the prediction model. Or the cutoff can be set lower. In an embodiment(s), this method is used with NCI COMPARE output data. In an embodiment(s) a correction(s) is applied if the same number of probes for each gene is not present in the database(s), wherein a proportionally lower observation cutoff is used for a gene with less probes. In an embodiment(s), and as a separate standalone method of the disclosure also (can be used with or without any other step herein), gene expressions found to be relevant are combined, using a method(s) as disclosed herein (refer to the disclosure section later with formulae for “GENE Q” and “GENE Y”), which can increase the predictive power above any individual gene expression used alone (e.g. refer
whose greater mRNA expression in cancer is observed to correlate with greater cancer susceptibility to anti-cancer activity of almitrine dimesylate:
whose lesser mRNA expression in cancer is observed to correlate with greater cancer susceptibility to anti-cancer activity of almitrine dimesylate:
In the gene lists below, generated from NCI COMPARE data, the cancer gene expression correlations are ranked by summing the |R| values, greater than 0.3, for each gene expression correlation to almitrine dimesylate susceptibility. This method variant simultaneously captures, in a single parameter (Σ|R|, where |R|>x, wherein x is 0.3 but can be higher or lower in other embodiments), the dual importance of amplitude, and frequency of observation, of a gene expression correlation. Preferably, for a gene, if/when all its correlations greater than x are not of the same sign, then this gene is dismissed from incorporation in this prediction model, for this drug. This method, at least with the 0.3 cutoff, more highly ranks some genes than the prior method, e.g. PTGS1, RBP1, JMJD1C, SORL1, ANXA11, CASP2, SYT1. In alternative disclosure embodiments this method, and/or component(s)/variant(s)/derivative(s) thereof, is applied with a different, optionally candidate (or failed candidate), optionally FDA/EMA/MHRA/PMDA/NMPA licensed, cancer drug(s), optionally one or more of the named/specified cancer drugs herein.
Ranking gene expression correlations to susceptibility/resistance to a cancer drug(s) by:
Σ|R| for a gene (defined prior), divided by the number of probes for that same gene in the gene expression database(s) used: Σ|R|/probe number. This method controls for the fact that different genes are likely to have a different probe number in the gene expression database(s). So, it yields a more equitable ranking, with the relative ordering of gene expression correlations likely to be more accurately represented. To illustrate by example: cancer gene expression correlations are ranked by summing the |R| values, greater than 0.3 (in other embodiments another value is selected, e.g. greater—closer to 1—for greater stringency), for each gene expression correlation to almitrine dimesylate susceptibility and dividing each Σ|R| by the number of probes for that gene in the gene expression database(s) used (data not shown). In alternative disclosure embodiments this method, and/or component(s)/variant(s)/derivative(s) thereof, is applied with a different, optionally candidate (or failed candidate), optionally FDA/EMA/MHRA/PMDA/NMPA licensed, cancer drug(s), optionally one or more of the named/specified cancer drugs herein.
In the gene lists below, generated from NCI COMPARE data, the cancer gene expression correlations are ranked by summing the |R| values, greater than 0.3 (in other embodiments another value is selected, e.g. greater—closer to 1—for greater stringency), for each gene expression correlation to almitrine dimesylate susceptibility but wherein only one transcript sequence, per gene, per gene expression database is used in the calculation of Σ|R|, wherein the transcript sequence with the highest value of |R| is selected for each gene per database. This stipulation controls for the fact that different genes can have different numbers of transcript sequence variants, each probed for, in any given gene expression database. Even with this control, there is still margin for bias if one or more genes are missing from one or more of the gene expression databases utilized, optionally which can be controlled for by only utilizing gene expression databases that contain data for all the same genes (data not shown). However, although these biases can impact the relative ordering of the gene expression correlations to drug(s) susceptibility/resistance this is not so important. I would argue. More useful is when all the different transcript sequences for each gene are considered, and are not controlled away, then the most highly ranked gene expressions, assuming a reasonably stringent cut-off value of R| is selected, are likely to be extremely valid and reliable. This is valuable. So, the method variant introduced by this section is not actually the most favoured for many use cases. In alternative disclosure embodiments this method, and/or component(s)/variant(s)/derivative(s) thereof, is applied with a different, optionally candidate (or failed candidate), optionally FDA/EMA/MHRA/PMDA/NMPA licensed, cancer drug(s), optionally one or more of the named/specified cancer drugs herein.
Gene entries in the table below: their gene expression correlates (consistently positively or negatively) with the anti-cancer activity of almitrine dimesylate. Both when their gene expression is assayed by a microarray or RNA-seq method. Indeed, for all those shown in this table, with both Affymetrix microarray mean and RNA-seq data (either composite and/or an individual isoform[s]) from CellMiner: |R|>0.3. Moreover, |R|>0.3, with microarray data from the NCI COMPARE database, multiple times, and always of same sign. Note that each Affymetrix microarray related value from CellMiner is a mean of 4 different Affymetrix microarray data sets. So, given all this, the gene expression correlations presented in this table are extremely robust.
To conserve space in the following table, only the gene isoforms with correlation |R|>0.3 are shown. Where only one gene isoform exists in the CellMiner database, “Only one” is written in the isoform column. In the table below: (A) Number of correlation observations to almitrine dimesylate anti-cancer activity, at |R|>0.3, in six selected (the most reliable) NCI COMPARE microarray data sets. (B) This is a daughter of column (A): sum of correlation (|R|) values at |R|>0.3. (C) Correlation (R) between mean gene expression (log 2[intensity], using the one microarray data set in CellMiner sourced using a microarray chip made by Agilent) and the mean[1-5]dose(10 μM) parameter (defined prior herein). (D) Correlation (R) between mean gene expression (log 2[intensity], mean of the 4 different microarray data sets in CellMiner produced using microarray chips made by Affymetrix) and mean[1-5]dose(10 μM). (E) Correlation (R) between RNA-seq composite gene expression (log 2[FPKM+1]) and mean gene expression (log 2[intensity], mean of the 4 different Affymetrix microarray data sets), all from CellMiner. (F) Correlation (R) between RNA-seq composite gene expression (log 2[FPKM+1]) and mean[1-|5-]dose(10 μM). (G) Correlation (R) between RNA-seq isoform(s) (log 2[FPKM+1]) and mean[1-/5-]dose(10 μM).
Refer to
In the table above there are a few cases where the composite RNA-seq value doesn't correlate as well as an individual isoform(s). An especially good example of this is with DDX49, wherein the composite correlation is very poor, but the correlation of a single isoform (NR_033677) is good. In some disclosure embodiments, a cancer patient(s)/subject(s) is selected for almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) administration on the basis of their level of expression of one or more isoforms (considered independently and/or in combination e.g. combined by some mathematical operation(s); optionally one or more isoforms mentioned herein) in their normal and/or cancer cells. There are some cases not shown in the table because, whilst their correlation to cancer susceptibility/resistance to almitrine dimesylate with microarray data is good, that with RNA-seq data, composite and individual isoform(s), is poor e.g. CD58, SMAD5 (shown below, with all isoforms present in CellMiner for these genes shown). If/when cancer susceptibility to almitrine dimesylate is to be assayed using RNA-seq technology it is better to not employ such gee(s).
In some disclosure embodiments, a cancer patient(s)/subject(s) is selected for almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) administration on the basis of the level of expression of one or more genes (considered independently and/or in combination, e.g. combined by some mathematical operation(s); preferably one or more genes mentioned herein) in their cancer (and/or normal) cells.
Optionally SCAF11, wherein higher SCAF11 gene expression in the subject's cancer, and/or wherein the greater the product of SCAF11 gene expression minus RAPH1 gene expression in a subject's cancer, is a (or the) drive to a decision to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to the subject. A method of, in the cancer (and/or a sample thereof) of a subject, measuring (optionally ex vivo/in vitro) the gene expression of a gene(s) that (as reported herein) positively correlates with the anti-cancer activity of almitrine dimesylate, and/or a gene(s) that (as reported herein) negatively correlates with the anti-cancer activity of almitrine dimesylate, optionally wherein this gene expression data is amalgamated/pooled/synthesized/mathematically operated upon (e.g. as disclosed herein), wherein this data is used to decide whether to administer (preferably a therapeutically effective amount of) almitrine dimesylate to the subject, optionally used to select which subject(s) to enter into a clinical trial of almitrine dimesylate for anti-cancer treatment, is componentry to this disclosure. In a disclosure method embodiment(s), if/when a subject's cancer(s) has high expression of a gene(s) that correlates with cancer susceptibility to almitrine dimesylate (e.g. as reported herein), and/or low expression of a gene(s) that negatively correlates with cancer susceptibility to almitrine dimesylate (e.g. as reported herein), almitrine dimesylate is administered to this subject. In a disclosure method embodiment(s), whether a gene expression in a cancer is high or low is decided by assaying whether it is higher or lower than the gene expression in a different cancer(s), e.g. in a different subject(s), e.g. in a cancer cell line(s), preferably a cancer(s) of same/similar type, and/or the gene expression in the normal tissue of the subject, preferably wherein this normal tissue is that from which the cancer derives, and/or by reference to a reference gene(s) expression in the subject's cancer and/or normal tissue(s). In a disclosure method embodiment(s), whether a gene expression in a cancer is high or low is decided by assaying whether it is higher or lower than the mean/median expression of that gene in a cohort of cancers, preferably of the same cancer type, and/or if the expression of that gene is in a top bracket (non-limiting e.g. half, quartile, decile, 20%, 15%, percentile etc.) of a group of cancers, preferably of same type, from different subjects. In a disclosure method embodiment(s), whether a gene expression in a cancer is high or low is decided by using the RPKM related method of [12].
In a disclosure method embodiment(s), whether a cancer, optionally inside a subject, is susceptible to almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) is predicted by comparing whether its gene expression of one or more of “GENE Q” is equal or higher (and/or substantially similar), and/or its expression of one or more of “GENE Y” is equal or lower (and/or substantially similar), than a cancer(s) (optionally a cancer cell line[s]) known to be susceptible to almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) treatment, optionally wherein this prediction is incorporated into an oral/audio/written/electronically encoded/computer medium report (optionally accessible on a computer/phone/electronic device, optionally transmitted over the internet/intranet, optionally transmitted by electromagnetic radiation) and/or is used to inform/dictate the decision as to whether to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to the subject that has this cancer, wherein if/when the subject's cancer is predicted to be susceptible to almitrine by this method then the subject is administered with a therapeutically effective amount of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof), optionally in co-therapy with another cancer treatment(s), optionally one or more licensed by the FDA and/or EMA. Conversely, in a different disclosure embodiment(s), whether a cancer, optionally inside a subject, is non-responsive/resistant to almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) is predicted by comparing whether its gene expression of one or more of “GENE Q” is equal or lower (and/or substantially similar), and/or its expression of one or more of “GENE Y” is equal or higher (and/or substantially similar), than a cancer(s) (optionally a cancer cell line[s]) known to be non-responsive/resistant to almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) treatment, optionally wherein this prediction is incorporated into an oral/audio/written/electronically encoded/computer medium report (optionally accessible on a computer/phone/electronic device, optionally transmitted over the internet/intranet, optionally transmitted by electromagnetic radiation) and/or is used to inform/dictate the decision as to whether to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to the subject that has this cancer, wherein if/when the subject's cancer is predicted to be resistant to almitrine by this method then the subject is not administered with almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof), optionally wherein the subject is administered a different cancer treatment(s) instead.
Componentry to this disclosure is a method to search for a correlation between a drug(s)/compound(s) activit[y/ies], in vitro and/or in vivo, upon a biological sample/tissue/system/organism, optionally anti-cancer activity, and the mean gene expression of more than one gene, which all correlate in the same direction (+ or −) with the drug(s) activity, and/or the product of the gene expression of a gene(s) subtracted from that of another gene(s), wherein the subtracted gene(s) has an opposite correlation, and/or the mean of gene expression of a gene(s) and one or more products of the gene expression of a gene(s) subtracted from that of another gene(s). Non-limiting examples of this method can be found in the present disclosure. With this method in hand, variations on this method will be apparent to those of the art, which are also componentry to the present disclosure. Once such a correlation has been elucidated it can be used as input into the decision whether to administer the drug(s) to a subject(s), e.g. if/when the subject's cancer has a high score for a gene expression function, which a drug(s) efficacy has been shown to scale/correlate with, then this drug(s) is administered to the subject.
In alternative disclosure embodiments, instead of, or in addition to, looking at cancer gene expression(s) by assaying at the transcript level, protein(s) amount is assayed, wherein with this data, the same gene(s) can be used to predict cancer susceptibility to almitrine dimesylate (transcript and protein amounts correlate [13]). A protein(s) amount can be assayed, for non-limiting example, using labelled antibodies to the protein. Especially convenient for a membrane protein, which has a component part(s) outside of the cell, e.g. CD58, CD133 etc.
“GENE Q” is any gene—as disclosed herein—(or mutant/variant thereof) whose greater expression in cancer (at DNA and/or mRNA and/or protein levels) correlates with greater cancer susceptibility to anti-cancer activity of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) (e.g. SCAF11, SLC38A1, MYC etc.), or is the mean gene expression of a number of such genes. “GENE Y” is any gene—as disclosed herein—(or mutant/variant thereof)—whose lesser expression in cancer (at DNA and/or mRNA and/or protein levels) correlates with greater cancer susceptibility to anti-cancer activity of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) (e.g. RAPH1, RAB23 etc.), or is the mean gene expression of a number of such genes.
“GENE Q” and “GENE Y” terms, when used in this disclosure, can in further embodiments, independently at each point of use, refer to additional entities comprising:
In some embodiments, “GENE Q” is the mean of a number (n) of “GENE Q”:
GENE Q=(ΣnGENE Q)/n
where GENE Q is (optionally normalized and/or mathematically operated upon e.g. logged, e.g. log 2) gene expression amount.
In some embodiments, “GENE Y” is the mean of a number (n) of “GENE Y”:
GENE Y=(ΣnGENE Y)/n
where GENE Y is (optionally normalized and/or mathematically operated upon e.g. logged, e.g. log 2) gene expression amount.
In some embodiments, “GENE Q” is “GENE Q” minus “GENE Y”:
GENE Q=GENE Q−GENE Y
where GENE Q and GENE Y are (optionally normalized and/or mathematically operated upon e.g. logged, e.g. log 2) gene expression amounts (or one or both is a mean thereof).
In some embodiments, “GENE Y” is “GENE Y” minus “GENE Q”:
GENE Y=GENE Y−GENE Q
where GENE Y and GENE Q are (optionally normalized and/or mathematically operated upon e.g. logged, e.g. log 2) gene expression amounts (or one or both is a mean thereof).
An outputted value of “GENE Q” by any formula herein can be inputted as input for “GENE Q” into any formula herein. An outputted value of “GENE Y” by any formula herein can be inputted as input for “GENE Y” into any formula herein. This can generate values of “GENE Q” and/or “GENE Y” with very strong predictive power for cancer susceptibility to anti-cancer activity of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof). For some examples refer to
A simpler example (
GENE Q=SCAF11−RAPH1,
where each gene name represents its (optionally normalized and/or mathematically operated upon e.g. logged, e.g. log 2) gene expression value.
Each gene expression value can be raw or, optionally, normalized with the expression of a reference or “housekeeping gene” (non-limiting e.g. [14]) or mean expression of multiple reference/housekeeping genes, or using some other normalization method of the art (e.g. z-scoring, e.g. RPKM method [12] etc.), and when gene expressions are combined/compared it is optimal to combine/compare like with like, e.g. normalized with normalized, wherein the same normalization method is used.
Given these formulae for “GENE Q” and “GENE Y”, modifications/derivatives (e.g. by changing a mathematical symbol[s]) will be apparent to those of the art.
A contemplated prediction score to predict cancer susceptibility to almitrine dimesylate=mean(gene expression of positively correlated gene[s])−mean(gene expression of negatively correlated gene[s])=mean(gene expression of “GENE Q” gene[s])−mean(gene expression of “GENE Y” gene[s]). Optionally this equation can be used with all, or just some fraction of, the “GENE Q” and “GENE Y” genes disclosed herein. Optionally this prediction score can be normalized to a scale from 0 to 100 by a linear transformation of the prediction score of a set of cancer samples from cancer patients. Optionally wherein this score is then used to select which of these cancer patients to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to: those with a prediction score above a selected cut-off value (where in some non-limiting embodiments the cut-off value is a number/integer selected from between w and 100, where w is a number selected from 50, 60, 70, 80, 90).
Assaying for overexpression of “GENE Q”, and/or underexpression of “GENE Y”, in a subject's cancer can be performed (without limitation) by measuring, ex vivo/in vitro, upon a sample(s) drawn from the subject, wherein the sample is (without limitation) derived from “liquid biopsy”, body wash (e.g. a lung wash sample), bodily fluid(s), tissue(s), normal tissue(s), cancer tissue(s) (e.g. sourced from tumour biopsy, e.g. fine needle/core biopsy), suspected cancer tissue(s), circulating cancer cell(s), cell line(s), circulating DNA fragments (cell-free DNA, cfDNA), fragmented RNA, bone marrow, urine, feces, buccal swab, saliva, plasma, serum or whole blood etc. or an extract or processed sample produced from any thereof. How to determine the amount of a DNA/RNA sequence(s), and/or mRNA transcript(s), and/or protein(s), in a biological sample (e.g. in a cancer sample) is well known to those of the art. For non-limiting example, measuring the amount of a DNA/RNA sequence by quantitative real time polymerase chain reaction (qPCR and/or RT-qPCR) and/or Serial Analysis of Gene Expression (SAGE) and/or RNA-Seq and/or whole-exome sequencing and/or Next generation sequencing and/or RNA sequencing and/or microarray chip technology (e.g. nucleic acid hybridization upon a solid-phase nucleic acid biochip array) and/or a “gene expression profiling” method(s), gene amplification in a cancer discovered by Fluorescent In Situ Hybridization (FISH), amount of a specific protein quantified by an immuno/antibody based technique wherein the antibody specifically binds to that protein type forming a protein-antibody complex, the amount of which is then measured etc. Some exemplary methods are used by the Beat-AML Master trial [12]. Componentry to this disclosure is a microarray chip with nucleotide sequences on it complimentary to the mRNA and/or cDNA of one or more of “GENE Q”, and/or one or more of “GENE Y”, and in a further embodiment(s) the method of using it as a custom chip to be used with a biological sample(s) from the subject to diagnose if cancer in the subject is likely to be susceptible to the anti-cancer activity of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof). Optionally wherein the chip is “Two-channel”, permitting two samples from the subject to be compared, e.g. one sample from normal, and other from cancer, tissue of subject.
Herein, when “GENE Q” is said to be overexpressed in a cancer this can refer (without limitation) to one or more of the following situations being true:
By the teaching above, when “GENE Y” is said to be underexpressed in a cancer this can refer (without limitation) to one or more of the above situations inverted i.e. lower value of “GENE Y” in the subject's cancer, in relation to one or more comparator(s) mentioned in (a)-(m), indicates a (greater) susceptibility to the anti-cancer activity of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof).
A (non-limiting) method to assay overexpression of a “GENE Q”, and/or underexpression of a “GENE Y”, in a subject's cancer is to compare expression in this cancer to expression in the subject's normal tissue, most preferably the tissue from which the cancer derives, wherein this sampling can optionally be done in vitro/ex vivo, and herein wherever this disclosure refers to measuring the expression of one or more of “GENE Q” and/or one or more of “GENE Y” in a subject's cancer, and/or sample(s) thereof, then in alternative embodiments, expression of one or more of “GENE Q” and/or one or more of “GENE Y” is measured in a subject's normal tissue(s) also and comparison is made to determine over- or under-expression of one or more of “GENE Q” and/or “GENE Y” in the subject's cancer, optionally wherein this cancer and normal tissue sampling is conducted in vitro/ex vivo with samples sourced from the subject and their cancer. There are other methods known to those of the art to determine over-/under-gene expression in a cancer, at the DNA (e.g. gene amplification and/or chromosome rearrangement placing gene under control of more active promoter) and/or transcript and/or protein levels, and their use, the use of one or more, in determining cancer susceptibility to almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) is contemplated by, and componentry to, this disclosure.
Also componentry to this disclosure is to monitor “GENE Q” and/or “GENE Y” during almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) treatment in a subject with cancer, wherein their decreasing overexpression (“GENE Q” case), and/or lessoning degree of underexpression (“GENE Y” case), in a sample(s) sourced from the subject, and/or subject's cancer, can be used to monitor anti-cancer treatment, or lack thereof (no change in “GENE Q” and or “GENE Y” expression), exerted by almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) administration and/or other administered drug(s).
Almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) for use in the treatment/amelioration/prevention/combat of a cancer (optionally CML/AML, or a lung cancer e.g. Non-Small Cell Lung Cancer [NSCLC]) overexpressing one or more of “GENE Q”, and/or underexpressing one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable.
Before starting almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) treatment, a test(s) can be carried out to make sure that the subject's cancer overexpresses one or more of “GENE Q”, and/or underexpresses one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable. Optionally, if/when one or more of these conditions aren't met, then almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) is not administered to the subject.
Almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) for use in a method for treating/ameliorating/preventing/combating cancer (optionally CML/AML, or a lung cancer e.g. NSCLC) in a subject, wherein the cancer is associated with amplification and/or overexpression of one or more of “GENE Q”, and/or underexpression of one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable, the method comprising administering (preferably a therapeutically effective amount of) almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to the subject.
Almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) for use in a method for treating/ameliorating/preventing/combating cancer (optionally CML/AML, or a lung cancer e.g. NSCLC) in a subject, wherein the subject's cancer has amplification and/or overexpression of one or more of “GENE Q”, and/or underexpression of one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable, and wherein the method comprises identifying that the subject's cancer has amplification and/or overexpression of one or more of “GENE Q”, and/or underexpression of one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable (optionally in vitro/ex vivo e.g. using a sample/derivative of the subject's cancer), and administering (preferably a therapeutically effective amount of) almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to the subject.
Almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) for use in a method of treating a subject with cancer (optionally CML/AML, or a lung cancer e.g. NSCLC), wherein the method comprises:
A method for diagnosing and treating almitrine sensitive cancer in a subject comprising: analysing a subject sample for the amplification and/or overexpression of one or more of “GENE Q”, and/or underexpression of one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable, wherein the subject is diagnosed with almitrine sensitive cancer if/when amplification and/or overexpression of one or more of “GENE Q”, and/or underexpression of one or more of “GENE Y”, is detected and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable; and
A method for treating cancer (optionally CML/AML, or a lung cancer e.g. NSCLC) in a subject comprising: requesting a test providing the results of an analysis to determine whether the subject's cancer has amplification and/or overexpression of one or more of “GENE Q”, and/or underexpression of one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable, and if it does, administering (preferably a therapeutically effective amount of) almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to the subject.
A method of diagnosing and treating cancer (optionally CML/AML or a lung cancer e.g. NSCLC) in a subject, said method comprising:
A method for diagnosing almitrine sensitive cancer in a subject, wherein the cancer is characterized by the amplification and/or overexpression of one or more of “GENE Q”, and/or underexpression of one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable comprising:
Method of selecting a subject(s) from a group of subjects with cancer (optionally CML/AML, or a lung cancer e.g. NSCLC) to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to, wherein a subject(s) is selected (optionally after being pre-selected on the basis of one or more other criteria) on the basis that their cancer, compared to other cancer(s) in the group, overexpresses one or more of “GENE Q”, and/or underexpresses one or more of “GENE Y”, and/or wherein the expression differential between at least one “GENE Q” (if/when more than one “GENE Q”, their mean is taken and used) minus at least one “GENE Y” (if/when more than one “GENE Y”, their mean is taken and used) is positive and preferably sizable, optimally wherein a selected subject(s) has greater positive differential in their cancer than those not selected. This can be a method of selecting a subject(s) for a clinical trial of almitrine anti-cancer activity.
A kit comprising almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) with material(s) to measure the expression (mRNA and/or protein) of one or more of “GENE Q”, and/or one or more of “GENE Y”, in the subject's cancer and/or in a sample from the subject's cancer (e.g. from a blood draw for a leukemia such as CML/AML), preferably with instructions for use, wherein the kit can be a companion diagnostic (requiring certain result for administration of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to subject) or a complimentary diagnostic (to inform decision making, but almitrine {and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof} can be administered regardless of the test result[s]).
The methodology described herein for almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) can be applied for a different drug(s), as contemplated by, and componentry to, this disclosure: using a sample derived from a subject and/or their cancer, to predict the cancer susceptibility/responsiveness/resistance to this drug(s), optionally a FDA/EMA/MHRA/PMDA/NMPA approved cancer drug(s), optionally a drug combination, wherein in a further embodiment(s) this is used to select which drug(s) to administer to the subject, which is then administered (preferably a therapeutically effective amount) to the subject.
Not all the following claims (or parts thereof) need be implemented: in alternative embodiments one or more claims, and/or part(s) thereof, are excluded, e.g. the normalization step(s) can be excluded so that the method is implemented with raw data. Herein, wherever “GENE Q” is referred to in the singular, in an alternative embodiment(s) it is referred to in the plural, and/or a claim can be iterated for different “GENE Q”, in series, or alternatively executed once for multiple “GENE Q” in parallel. Herein, wherever “GENE Y” is referred to in the singular, in an alternative embodiment(s) it is referred to in the plural, and/or a claim can be iterated for different “GENE Y”, in series, or alternatively executed once for multiple “GENE Y” in parallel.
The claims below also apply to “GENE Y” substituted in place of “GENE Q” but with a difference: less (rather than more) “GENE Y” protein(s) and/or “GENE Y” nucleic acid(s) and/or “GENE Y” gene copy number amount predicts [optionally greater] susceptibility/responsiveness of a subject's cancer to treatment with almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof).
In alternative embodiments, wherever almitrine is mentioned in the following claim set, a different drug's name (one of the drugs mentioned/taught herein) is substituted in its place, and the “GENE Q” and/or “GENE Y” mentions in these claims then relate to one or more of those (specified/taught herein) for that other drug:
A high almitrine dose (intravenous delivery of 459±155 mg almitrine dimesylate, infused within 24 hours, which is a multiple of the (presently) clinically employed 50 to 200 mg oral daily dose) can potentially increase blood [lactate] in subjects with poor/impaired liver function; those with normal liver function are without any adverse effect [16]. Deselecting subjects with poor/impaired liver function from almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) administration is an optional embodiment(s) of this disclosure. A disclosure embodiment(s) is to find an individualized upper bound for almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) dose in a subject, wherein the almitrine dose (e.g. daily) is increased until there is a notable increase in the subject's plasma [lactate] from a baseline recorded prior to the course of administration, wherein the continuation dose is then set to be some function of this e.g. equal or a proportion (e.g. half) of it, wherein the almitrine dose arrived at is heavily dependent upon the subject's liver capacity which can be measured, e.g. by assaying plasma [bilirubin], prior to the course of almitrine treatment and the starting almitrine dose set accordingly, wherein those with poor liver function are administered a lower almitrine dose, or no almitrine at all.
In alternative embodiments, the ordering of one or more steps is different than presented here and/or one or more steps are omitted.
A method to predict/utilize the susceptibility/responsiveness/resistance of a subject's cancer to almitrine treatment, disclosed prior, is a sub-method/example of a broader method disclosed herein/now:
A method to select a drug(s), optionally/preferably one or more of the named/specified drugs herein, optionally a licensed/approved cancer drug(s) by the FDA and/or EMA and/or MHRA and/or PMDA and/or NMPA (and/or a different country/jurisdiction in the world), to administer to a subject to treat/ameliorate/prevent/combat their cancer:
Detect/retrieve cancer gene expression (mRNA/cDNA/protein/combination thereof) correlation(s) (preferably wherein the correlation for each gene has been, or is, assayed multiple times) to susceptibility/resistance to the anti-cancer activity(s) of a drug(s).
optionally calculate “DIFFERENTIAL”={(ΣaGENE Q)/a}−{(ΣbGENE Y)/b};
In alternative embodiments the ordering of one or more steps is different than presented here, and/or one or more steps are omitted. Step (i) of PHASE (2) can be omitted such that “said sample(s)” in step (ii) is then replaced with “a sample(s) obtained from a subject”: this alternative is better before the European Patent Office (EPO). It is componentry to this disclosure to only conduct one or more of the steps and not all in their entirety. One salient stand-alone sub-set is PHASE (2), steps (i-vii), which could be performed by a contract testing laboratory to a hospital for example, or steps (viii-ix) that could be performed by a doctor/oncologist for example. All the steps in entirety could be performed by a clinical trial program for example, e.g. by a Master trial protocol such as used by the Beat AML trial [12].
Using the method, or only part(s)/step(s) thereof, with a FDA/EMA/MHRA/PMDA/NMPA approved drug, optionally but not restrictively licensed for anti-cancer use, is componentry to the disclosure.
In this method, gene expression can be assayed at one or more of the mRNA, cDNA and protein levels, optionally with their data pooled to give an amalgamated value for each gene expression. In an alternative embodiment(s), the method is applied with microRNA and/or non-protein coding RNA sequence(s) instead of, or in addition to, gene expression. Some of the cutoffs in the method have been expressed in terms of greater than, or less than, but instead, at one or more places, a cutoff(s) in terms of equal or greater than, or equal or less than, is also contemplated by, and componentry to, the disclosure.
In a method embodiment(s), the level of one or more of “GENE Q” and/or “GENE Y” protein(s), and/or one or more of “GENE Q” and/or “GENE Y” nucleic acid(s) (DNA/RNA), is measured ex vivo/in vitro in a sample(s) taken from said subject. Wherein the sample(s) can be derived (without limitation) from one or more of “liquid biopsy”, body wash (e.g. a lung wash sample), bodily fluid(s), tissue(s), normal tissue(s), cancer tissue(s), suspected cancer tissue(s), circulating cancer cells, cell line(s), urine, feces, plasma, serum and/or whole blood, or an extract or processed sample produced from any thereof.
How to determine the amount of a DNA/RNA sequence(s), and/or an mRNA transcript(s), and/or protein(s), in a biological sample is well known to those of the art. And all such methods are contemplated by, and componentry to, this disclosure.
In an embodiment(s), step (ii) is replaced with, or added to, a step assaying for gene(s) amplification, e.g. detected (or lack thereof) by Fluorescent In Situ Hybridization (FISH), wherein an alternative step (iii) is to take a gene amplification to be synonymous with overexpression of that gene(s) and, optionally, a cancer with this amplification is then assumed to have above the threshold reference value(s) for this gene in step (iv). But wherein, in an alternative embodiment(s), this assumption is tested by conducting the original step (ii) for this gene, and proceeding with following steps accordingly.
In an embodiment(s), a correction(s) is applied if/when the same number of gene expression probes for each gene is not present in the gene expression database(s). For non-limiting example, a proportionally lower observation cutoff is used for a gene(s) with less probes. If/when the |R| summation method is used, |R| summation for a gene with less probes is increased by multiplication with a constant proportional to the probe number disparity between this gene and another(s), which it is compared to; alternatively |R| summation for a gene with more probes is decreased by division with a constant proportional to the probe number disparity between this gene and another(s), which it is compared to, wherein one or both these methods, optionally with different constants for different genes as applicable to probe number disparit[y/ies], is used to enable fair comparison across all the gene expressions considered.
Componentry to this disclosure is for one or more of these method steps, or for any one or more of the method steps herein, disclosed by this disclosure, to be implemented by a computer operation(s)/program(s), optionally a phone app. At any place that “method” is written/implied herein, in alternative embodiments this is substituted with “computer implemented method”. Any method herein (or sub-method(s) thereof), implemented by a computer, is componentry to this disclosure.
A kit(s) for implementing all or part of this method(s)—a kit(s) to select a drug(s) to treat/ameliorate/prevent/combat a subject's cancer, and/or a kit(s) to predict/diagnose the susceptibility/responsiveness/resistance of a subject's cancer to a drug(s)—is componentry to this disclosure, wherein a kit for implementing PHASE (2) is especially preferred comprising, optionally equipment(s) to draw a biological sample(s) from the subject, reagent(s) necessary for measuring the level of one or more “GENE Q” and/or “GENE Y” protein/DNA/cDNA/RNA/mRNA in a biological sample(s) from a subject, further comprising comparator information/module which comprises (optionally derived from PHASE (1)) a standard value, or a set of standard values, to which the level of “GENE Q” and/or “GENE Y” protein/DNA/cDNA/RNA/mRNA in the sample is compared, optionally with instruction(s)/direction(s) for which drug(s) to administer on the basis of the comparison. Optionally the comparator module is a computer and/or software, optionally which can be updated, optionally by information/data (optionally encrypted) sent across the internet, which has the benefit that the standard/threshold value(s)/recommendation(s)/direction(s) can be updated as further drug(s) are investigated/developed. Optionally wherein the PHASE (1) method step is iterated, optionally in a systematic program, to generate further data that can be used to update the comparator module, optionally wherein a subscription is required to receive an information update(s).
In a disclosure embodiment(s), PHASE (1) of the method is conducted with human subjects, e.g. using a sample(s) drawn from each subject, from which it is observed, across different subjects (more is better), which gene(s) expression in a subject's cancer correlates with observed anti-cancer activity in the subject (and/or in a sample of the subject's cancer grown in vitro and/or grown in an animal/mouse avatar), or lack thereof (resistance), to a drug(s). Optionally wherein the subjects are in a clinical trial(s), e.g. the BEAT AML trial [12]. Optionally wherein the subject(s) of PHASE (2) of the method are in the same trial or a daughter of it. In this way, PHASE (2) can become PHASE (1) for a later PHASE (2), optionally iterated, and so the method iteratively becomes better.
In a disclosure embodiment(s), PHASE (1) of the method is the BEAT AML trial [12] (itself or its protocol {or variant thereof} replicated/modified) wherein its output data upon some subjects being administered almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) is used with a method herein, or other method (e.g. as in [12]), to find cancer gene expression (and/or gene mutation(s) and/or cytogenetic abnormalit[y/ies]) correlation(s) to susceptibility to the anti-cancer activity of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof), optionally which is then used to select some, and exclude other, further subjects to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) for anti-cancer treatment.
Using a biomarker(s) (non-limiting e.g. gene(s) expression, mutation(s), SNP(s), epigenetic/methylation marker(s), cytogenetic abnormalit[y/ies], metabolite(s) level, enzyme(s) activit[y/ies], antigen(s), cell surface protein(s), genomic/transcriptomic/proteomic/metabolomic marker(s), cell-free DNA(s) etc.) measured in a subject, and/or in a biological sample(s) sourced from a subject, to decide/determine (and/or as an input into deciding/determining) whether to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to treat/ameliorate/prevent/combat cancer in the subject is componentry to this disclosure.
At any point that almitrine is mentioned/implied herein, in alternative embodiments this mention/implication is substituted with a different drug(s) mentioned/specified herein, and correspondingly—in each case—the mentions of “GENE Q” and “GENE Y” and “DIFFERENTIAL” herein are substituted for those applicable/taught for this other drug(s).
Any method used/disclosed herein to source and select cancer gene expression correlations to almitrine dimesylate susceptibility/resistance (biomarkers), for incorporation into a prediction model of cancer susceptibility/resistance to almitrine dimesylate, is contemplated with a different drug mentioned/specified herein, for generating a prediction model of susceptibility/resistance to that drug. And the resulting correlations (biomarkers) and prediction models are hereby disclosed herein. And the administration of a therapeutically effective amount of a drug(s) to a subject, by following/using such a prediction model, is herein contemplated and componentry to this disclosure.
Acquiring cancer gene expression biomarkers for susceptibility/resistance to Ibrutinib drug (NSC number: 761910). Went to https://dtp.cancer.gov/pubic_compare/ and inputted its NSC number, in the box labelled for inputting the NSC number. Clicked search. Then, in the page thereafter, clicked the circle with C inside of it (the symbol for NCI COMPARE). Then, in the page thereafter, for “target set name”, I selected “MOLTID_GC_SERIES_MICROARRAY_ALL”, which, as aforementioned, is a favoured set/collection of a number of cancer gene expression databases. For count of results to return I selected the maximum, which is presently 2,000. For minimum correlation I selected the smallest minimum correlation value, at or above 0.3, that returned less than the maximum number of returnable results in the system (which is presently 2,000). Which was found by trial and error to be 0.4. Thereafter I retrieved the results (with minimum correlation=0.4), of cancer gene expression correlations to the drug's anti-cancer activity (in NCI-60 testing), downloading them in Microsoft Excel format. There were 1,423 entries. I then searched for those genes that were present the most times in these results. By finding how many times each gene present was observed in this data set, and ranking them, from most frequently observed to least. Then setting a cut-off number of observations, above which the gene was included in the prediction model, and below which it wasn't. Wherein a different cut-off was used for those genes whose expression correlates positively, and for those whose expression correlates negatively. The cut-off number selected in each case respectively determining how many “GENE Q” (gene whose expression positively correlates with drug's anti-cancer activity) and “GENE Y” (gene whose expression negatively correlates with drug's anti-cancer activity) I pulled from this data, to use in my prediction model. In this example, I used high cut-offs such that only a very small number of “GENE Q” and/or “GENE Y” were retrieved. Which, because of their high frequency of observation, are likely to be incredibly robust. These are presented below. This method is very distinctive from what one of the art would do: they would look to see which gene(s) had the highest correlation R score and build a prediction model using this/these, without any consideration of frequency of observation. They would select for highest value of R, instead of selecting for highest frequency of observation over a modest threshold value of R. Probably not even seeing all these results because when running NCI COMPARE they would set the minimum correlation threshold as high as they could, so long as it still returned results. And utilize these genes returned. Possibly searching therein for the particular gene(s) with highest correlation value of R, and using this gene(s). They wouldn't set the minimum correlation as low as I did, such that 1,423 results are returned. A huge number. If the minimum correlation is instead set to 0.6, just 32 results are returned. If the minimum correlation is instead set to 0.65, just 2 results are returned (only one of which is found below: PDZK1IP1, which is only the 12th ranked gene by this present method).
In some embodiments, “DIFFERENTIAL”=MACF1−VPRBP, or “DIFFERENTIAL”=MACF1−PARD3, or “DIFFERENTIAL”={mean(MACF1,PARD3)}−VPRBP, or “DIFFERENTIAL”={mean(MACF1,PARD3,PDXK,CD24)}−{mean(VPRBP, C1QBP)}. In some embodiments, “GENE Q” is mean(MACF1,PARD3). In some embodiments, only “GENE Q” is used in the prediction model, and not “GENE Y”.
Acquiring cancer gene expression biomarkers for susceptibility/resistance to Osimertinib drug (NSC number: 779217). Used the method as above. Except that in this case the smallest minimum correlation, at or above 0.3, which returned less than the maximum number of returnable results in the system (which is presently 2,000) was 0.35. With this, 1640 results returned.
Acquiring cancer gene expression biomarkers for susceptibility/resistance to Palbociclib drug (NSC number: 758247). Used the method as above. Except that in this case, the smallest minimum correlation, at or above 0.3, which returned less than the maximum number of returnable results in the system (which is presently 2,000) was 0.45. With this, 1785 results returned.
Acquiring cancer gene expression biomarkers for susceptibility/resistance to Enzalutamide drug (NSC number: 766085). Used the method as above. Except that in this case, the smallest minimum correlation, at or above 0.3, which returned less than the maximum number of returnable results in the system (which is presently 2,000) was 0.4. With this, 1871 results returned.
“GENE Q”: MMP14 (correlation score, R, greater than +0.4 observed 12 times). GAP43 (correlation score, R, greater than +0.4 observed 11 times). HIPK2 (correlation score, R, greater than +0.4 observed 9 times). MYO10, RHEB, MDFIC, HNMT (correlation score, R, greater than +0.4 observed 8 times for each). DAD1 (correlation score, R, greater than +0.4 observed 7 times for each).
“GENE Y”: NUP210 (correlation score, R, greater than |-0.41 observed 14 times). HNRNPD (correlation score, R, greater than |-0.41 observed 11 times). PNISR (correlation score, R, greater than |-0.41 observed 8 times).
After each drug name, in brackets, is the selected threshold value of x, and after each gene name, in brackets, is the number of observations of correlation (n) above that threshold value of x, wherein in most cases y was set at 7 (but in occasional, isolated cases decreased to 6 or 5 when more biomarkers were desired for a drug): Enasidenib (0.35): “GENE Q”: SLC1A4 (15), SCD (7); “GENE Y”: PRKAA2 (11), IGFBP7 (10), MAP1B (8), WARS2 (7), PTPRB (7), JAG1 (7), TWSG1 (7). Ivosidenib (0.3): “GENE Q”: BGN (8), SFRP2 (7), ZHX3 (7). Vorasidenib (0.55): “GENE Q”: ENAH (15), FLNA (15), MAP1B (14), PPP2R3A (14), CFL2 (10), KIF13A (10), RBMS3 (9), CYR61 (9), NFIB (8), PEA15 (8), THBS1 (8), BACE1 (7), PFN2 (7), CAV1 (7), TRPC1 (7), CNN3 (7), PALLD (7); “GENE Y”: NUP210 (9), RSL24D1 (7), TGDS (7), SVIP (7). Fluorouracil (0.4): “GENE Q”: NDUFB10 (5), RNPS1 (5), NOLC1 (5); “GENE Y”: MACF1 (17), BACE1 (8), UTRN (7), H2AFV (7). Bafetinib (0.4): “GENE Q”: RAB27A (17), GYPA (14), GYPB (13), RHAG (12), HBA1 (12), PRKCB (11), HIC2 (11), MAPK1 (10), PPIL2 (10), PKLR (9), THEM4 (9), RFESD (8), UROD (7), GFI1B (7); “GENE Y”: SMAD1 (12), ACTB (12), MYOF (12), KTN1 (11), ZFP90 (11), PARD3 (11), ANXA2 (9), RBMS2 (9), TPM1 (9), AJUBA (9), ALCAM (8), GADD45B (8), PAWR (8), SPIN1 (8), RSPRY1 (7), NFIB (7), SPTAN1 (7), RRAS2 (7), ACTG1 (7). Bortezomib (0.4): “GENE Y”: CAPS (11), GPM6A (11), ERBB2 (11), CHD9 (11), CLOCK (10), PTPRB (10), MFAP3L (8), SOX2 (8), WARS2 (8), SEC22B (8), ARHGEF4 (7), GFAP (7), RUNX2 (7), AGPS (7), EPHA3 (6), AMPD1 (5). Cabozantinib (0.35): “GENE Q”: CALU (19), MMP14 (13), FKBP7 (10), PXDN (10), CALD1 (10), CKAP4 (8), THBS2 (8), FAM114A1 (7), CREB3L1 (7), TGOLN2 (7), TBC1D8B (7), NAV1 (7); “GENE Y”: EZR (11), JAG2 (8), PM20D2 (7), RAB4A (7), EIF3M (7), RNGTT (6). Carfilzomib (0.35): “GENE Q”: IK (7), FAM122B (7), BUB3 (7), FBXO45 (7); “GENE Y”: SNRPN (29), ABCB1 (14), NR2F2 (14), PLCB4 (11), TRAMI (10), RAB3B (8), HCFC1R1 (7). Cisplatin (0.4): “GENE Q”: PTPRC (14), IKZF1 (12), KDMSA (9), SLA (9), ANP32E (8), CEP350 (8), CHD1L (8), RAB33A (7), WAS (7), ACAP1 (6), ARHGAP30 (6); “GENE Y”: KLF3 (11), EHF (11), AGAP1 (10), ITGB4 (10), EPB41L4B (9), MYO1G (7), CTDSPL (6), DLG3 (6). Cobimetinib (0.45): “GENE Q”: PARVB (14), ETV5 (13), DUSP4 (12), MITF (11), RASSF3 (11), ROPN1 (10), SPRED1 (9), DUSP6 (8), GPR56 (8), INPPSF (8), PRKCD (8), SOX10 (8), THEM4 (8), PLEKHB2 (7), SORD (7), TYRO3 (7); “GENE Y”: NUCKS1 (12), ZFP90 (7). Crizotinib (0.4): “GENE Q”: PTPRC (11), KIR2DL1 (11), PDE4DIP (10), ALK (9), PRPF40A (9), KIR2DL4 (9), HLA-DRB1 (9), MXD1 (9), PSD4 (8), SERPINB3 (8), SERPINB4 (8), SOCS1 (8), TLE3 (7), TMEM260 (7); “GENE Y”: RBMS2 (16), LAPTM4B (11), KIF13A (10), RRAS2 (8), ACTN1 (7), CFL2 (7), DHCR24 (7), RAI14 (7), SORBS3 (7). Dabrafenib (0.45): “GENE Q”: SOX6 (16), PARVB (15), GAS7 (13), SNCA (13), SASH1 (12), MITF (11), MLANA (10), SGCD (10), RASSF3 (10), ROPN1 (10), DCT (9), FAM210A (9), ZFP106 (9), INPP5F (9), STX7 (9), BACE2 (8), RXRG (8), SOX10 (8), CAPN3 (8), DUSP4 (8), GSN (8), THEM4 (7), ACSL3 (7), EDNRB (7), TYR (7), ITGA9 (7), VAT1 (7), LZTS1 (7), QPCT (7), SPRED1 (7), ACP5 (6); “GENE Y”: GEMIN2 (12), SLC38A1 (11), HNRNPC (7). Dasatinib (0.45): “GENE Q”: VCAN (16), CFLAR (14), BIN1 (12), PARD3 (12), RBPMS (12), TFPI (11), LPP (10), CAV2 (10), PTRF (10), TLDC1 (10), RTN4 (9), PRKCA (9), ROR1 (9), TRIM8 (9), TPM1 (8), EXT1 (8), TRPC1 (8), MICAL2 (8), NR2F2 (8), NRIP1 (8), NRP1 (7), TWSG1 (7), AHNAK (7), AJUBA (7), AXL (7), EGFR (7), RRAS2 (7); “GENE Y”: IDH3A (9), WDR61 (8), GK (8), SLC25A11 (8), CBX4 (7), SMCR7L (7). Daunorubicin (0.35): “GENE Q”: PTPRC (14), IKZF1 (13), ZNRF1 (11), ACTG1 (9), PTPN22 (8), SLA (8), ARHGAP19 (7), PTPRN2 (7), MPHOSPH9 (7), MYO1G (7), NSL1 (7); “GENE Y”: ABCB1 (14), NR2F2 (13), PTK2 (9), ACTN1 (8), ITCH (7), LITAF (7), RBMS2 (7), TSTA3 (7). Docetaxel (0.4): “GENE Q”: IK (7); “GENE Y”: FAM19A5 (13), APOE (12), PLXNC1 (11), ZMAT3 (10), CDK2 (10), ATP6VOC (9), IVNS1ABP (9), BCAN (8), ARHGEF40 (7), MDM2 (7), NDE1 (7), NPR1 (7), PRTG (7), USP53 (7). Doxorubicin (0.35): “GENE Q”: ZNRF1 (9), AR (8); “GENE Y”: ABCB1 (14), PTK2 (11), NR2F2 (10), PTPN4 (8), TSTA3 (7), RBMS2 (7), HOOK1 (7), ITCH (7). Epirubicin (0.4): “GENE Q”: ZNRF1 (7); “GENE Y”: ABCB1 (14), PTK2 (11), NR2F2 (10), RBMS2 (7). Erlotinib (0.4): “GENE Q”: PAX8 (21), RBMS2 (16), PARD3 (12), RBPMS (11), CAV2 (11), RAB3B (11), LAPTM4B (10), LEPROT (9), FAM45A (9), GLRB (9), TLDC1 (9), PPP1R9A (8), MPZL1 (8), CDR2L (8), CNIH4 (8), RASAL2 (8), RBKS (8), PDZK1IP1 (7), CD24 (7), EGFR (7), ALDH3B1 (7), DCAF6 (7), RRAS2 (7), VCAN (7); “GENE Y”: TMEM161B (10), DKC1 (9), RBM10 (8), THOC2 (8), GTF3A (7), ACAA1 (6). Etoposide (0.4): “GENE Q”: TRPS1 (12), GATA3 (11), MUC1 (11), ESR1 (10), PDCD4 (10), PRLR (10), SPDEF (9), MED13L (8), SCAMPI (8), PTGER4 (7), CRNDE (7), FOXA1 (7); “GENE Y”: ANXA2 (10). Gefitinib (0.35): “GENE Q”: GLS (14), PAX8 (11), RBPMS (11), SPP1 (10), ANXA4 (9), MPZL1 (9), PPP1R9A (8), PDZK1IP1 (7), PON2 (7); “GENE Y”: BRD8 (8), ARIH2 (8). Imatinib (0.35): “GENE Q”: GYPB (15), GYPA (14), HIC2 (13), GATA2 (13), PRKCB (13), HBA1 (12), RHAG (12), IGF1 (9), PPIL2 (9), PKLR (9), UROD (9), RFESD (8), CRKL (8), NUP214 (7), GFI1B (7), ACSM3 (7), CISH (7), CPED1 (7), RHD (7); “GENE Y”: TLE1 (9), TWF1 (8), FAM45A (7). Ixabepilone (0.35): “GENE Q”: HAPLN1 (13), DTNA (7), ADO (7); “GENE Y”: RASAL2 (13), WWC1 (9), ATXN10 (8), TAOK1 (7), ZAK (7). Lapatinib (0.35): “GENE Q”: PAX8 (11), PDZK1IP1 (7), PPP1R9A (7); “GENE Y”: CUL4B (10). Lenvatinib (0.4): “GENE Q”: GBP1 (11), TPM1 (10), PXDN (10), TRPC1 (9), TMCC1 (8), BMP1 (8), CCDC80 (8), CYR61 (8), MICAL2 (8), PEA15 (8), PLOD2 (8), PRSS23 (8); “GENE Y”: AMD1 (11). Mitomycin (0.45): “GENE Q”: CD44 (15), MAP1B (14), TAOK1 (11), CAMSAP2 (10), SMARCA1 (10), HFE (9), OSMR (8), CKAP4 (8), LARP6 (8), CAV1 (7), PFN2 (7), RTN4 (7); “GENE Y”: DCAF11 (7), NUP210 (7), SMARCC1 (7). Nilotinib (0.3): “GENE Q”: PRKCB (18), GYPB (15), GYPA (14), HIC2 (13), RHAG (13), HBA1 (12), PPIL2 (11), IGF1 (9), MAPK1 (8), PKLR (8), RFESD (8), GFI1B (7), NUP214 (7); “GENE Y”: TRIO (11), NMT2 (10), CAMSAP2 (9), APBB2 (9), AJUBA (8), ITGA3 (8), SMARCA1 (8), ANXA2 (7), PFN2 (7), PRKAG2 (7), VEZF1 (7). Olaparib (0.35): “GENE Q”: ELL2 (14), IGFBP5 (14), D102 (13), GLIPR1 (11), FGF5 (10), HGF (10), PDE5A (10), NAMPT (9), CLU (8), LTBP1 (8), VAMP4 (8), WNT5A (8), PCDH18 (7), BCL3 (7), CDK14 (7), HIPK2 (7), S1PR3 (7), YIPF5 (7); “GENE Y”: CTDSPL (9). Oxaliplatin (0.45): “GENE Q”: RPL4 (9), DDX18 (9), HNRNPA1 (8), USP7 (8), HNRNPC (7); “GENE Y”: HOMER3 (8), GNA11 (7), LPP (7), NACA (7), RSL24D1 (7). Paclitaxel (0.35): “GENE Q”: FBXO45 (12), ARPC5L (8), BUB3 (7); “GENE Y”: PLCB4 (12), ABCB1 (12), MDM2 (11), NR2F2 (11), RAB3B (11), KIAA1033 (10), RASAL2 (9), APOE (8), TRPM1 (8), UGCG (8), FAS (7), LEPROT (7), PDZK1IP1 (7), TRAM1 (7), ZMAT3 (7). Pazopanib (0.35): “GENE Q”: GBP1 (8), HLA-E (7), OPTN (7), SP 110 (7), TRIM38 (7); “GENE Y”: SESN3 (15), CAPS (11), ETNK1 (11), SOX2 (9), DACH1 (8), GCLC (8), GSTTI1 (7), SRSF8 (7). Pemetrexed (0.35): “GENE Q”: THUMPD1 (8), METAP2 (8), ESD (8), CDT1 (7), NAP1L1 (7); “GENE Y”: MAP3K13 (8), SOX13 (7). Rapamycin (0.4): “GENE Q”: PLOD2 (9), FERMT2 (8), PDLIM7 (8), PKIA (8), RBMS1 (8); “GENE Y”: PTCD3 (10), LRPPRC (9), AKAP1 (8), PRPS2 (7), SLC35D2 (7), SORD (7). Ruxolitinib (0.4): “GENE Q”: TPM1 (15), VCAN (15), OPTN (12), CCDC80 (11), EHD2 (10), LTBP2 (9), CYP1B1 (8), PAPSS2 (8), PLOD2 (8), NNMT (7), CDH2 (7), FBN1 (7), GALNT1 (7), HRH1 (7), INHBA (7), JAKI (7), PAM (7). Selumetinib (0.4): “GENE Q”: PARVB (15), ETV5 (12), DUSP4 (12), MITF (11), RASSF3 (10), ROPN1 (10), STX7 (10), GSN (9), SPRED1 (9), RXRG (8), SGCD (8), STK10 (8), SNCA (8), PRKCD (8), ZFP106 (8), SOX10 (8), CREBL2 (8), MLANA (8), GPR56 (8), LYST (8), CAPN3 (7), FAM210A (7), SORD (7), THEM4 (7), TMTC2 (7), DUSP6 (6), ERBB3 (6); “GENE Y”: NUCKS1 (17), KTN1 (10), TBL1X (10), GADD45B (9), ZFP90 (9), BAD (8), MALAT1 (8), SPIN1 (8), GEMIN2 (7), MYL6 (7), VEZT (7). Sonidegib (0.4): “GENE Q”: JMJD6 (6), LAPTM4B (6); “GENE Y”: STK17B (7), N4BP2L1 (6). Sorafenib (0.4): “GENE Y”: PARD3 (10), AK4 (9), PAX8 (8), CD24 (7), LAMC2 (7), PPP1R9A (7), TSPAN2 (7). Sunitinib (0.4): “GENE Q”: HNF4A (8), ADD3 (7); “GENE Y”: IL6ST (11), EML1 (8), RDX (8). Tamoxifen (0.45): “GENE Q”: ANAPC5 (11), GTF3A (11); “GENE Y”: RBMS2 (12), ANXA2 (12), NNMT (11), RRAS2 (9), CRIM1 (8), CLIP4 (8), ITSN1 (8), DST (7), WWC1 (7). Temsirolimus (0.35): “GENE Q”: FAS (16), MR1 (9), CUX1 (7), TOR1A (7); “GENE Y”: TCF7L2 (18), EML4 (14), CTBP2 (12), ITGA6 (11), ADD3 (10), ASXL2 (8), GPD2 (8), AGAP1 (7), CHKA (7), ITGB4 (7), SLC16A5 (7), SORD (7). Trametinib (0.45): “GENE Q”: DUSP4 (11), RASSF3 (11), SPRED1 (8); “GENE Y”: NUCKS1 (9). Vemurafenib (0.45): “GENE Q”: SGCD (18), RAB27A (14), SNCA (14), MLANA (12), PARVB (12), GAS7 (11), DUSP4 (11), MITF (11), SASH1 (11), RASSF3 (10), ROPN1 (10), TRIB2 (10), AP1S2 (9), CSPG4 (9), LZTS1 (9), ETV5 (9), ZFP106 (9), SOX10 (8), BACE2 (8), RXRG (8), GSN (8), ITGA9 (8), ASAH1 (7), FAM210A (7), INPP5F (7), MREG (7), TYR (7); “GENE Y”: SLC38A1 (8), GADD45B (7). Vinblastine (0.35): “GENE Q”: HNRPLL (8); “GENE Y”: LEPROT (14), ABCB1 (12), PLCB4 (12), FAM49A (11), RAB3B (11), UGCG (11), NR2F2 (10), PARD3 (9), PAX8 (8), CLDN1 (7), CLEC4E (7), DENND3 (7), EPS8L2 (7), PDZK1IP1 (7), RBMS2 (7), TMEM178B (7). Vincristine (0.35): “GENE Q”: THOC2 (7); “GENE Y”: SCAND1 (15), CXCL5 (10), TGM2 (9), PON2 (9), NR2F2 (9), FGB (9), TMEM106B (9), SCEL (8), RBMS2 (7), FGA (7), FGG (7), CYP4F3 (7), DIRAS3 (7), AKAP13 (7), CHST9 (7), TLDC1 (7). Anastrazole (0.3): “GENE Q”: SSX3 (16), PPFIA1 (14), ST13 (11), FCRLA (10), HLA-DRB1 (9), RANGAP1 (9), NRXN1 (8), SMDT1 (8), SSX2 (8), XPNPEP3 (7), SSX1 (7), DESI1 (7), FGF13 (7), GBA3 (7), MYOZ2 (7), NHP2L1 (7), POLR3H (7), SSX4B (7); “GENE Y”: UBXN4 (6). Bendamustine (0.35): “GENE Q”: CDK6 (21), IKZF1 (16), FYB (15), TFDP2 (15), CD84 (11), GIMAP5 (11), NKAIN4 (11), ITGA4 (11), CASP2 (11), BCL11B (9), LDLRAD4 (9), GIMAP6 (8), CD6 (8), CXCR4 (8), ERG (8), LAT (8), CFTR (7), CRABP1 (7), IL4 (7), PDE7A (7); “GENE Y”: ANXA2 (11), AGAP1 (10), CTBP2 (10), CTNNA1 (8), PTK2 (7). Bleomycin (0.4): “GENE Q”: RORA (13), SOCS3 (13), PDE4DIP (11), GAS1 (10), ALK (10), HIPK2 (10), HLA-DRB1 (9), KIR2DL4 (9), SOCS1 (9), SERPINA1 (8), SERPINB3 (8), SERPINB4 (8), TRPC1 (8), CD274 (7), HLA-DQB1 (7), HTRA3 (7), IRF5 (7), TMEM260 (7); “GENE Y”: SORL1 (9), CHKA (8), RDH13 (7). Carmustine (0.35): “GENE Q”: GYPB (15), GYPA (14), HIC2 (13), PRKCB (13), HBA1 (12), RHAG (12), UROD (11), IGF1 (10), PPIL2 (10), RFESD (8), RHD (8), NUP214 (7), ACSM3 (7), CPED1 (7), GFI1B (7), PKLR (7); “GENE Y”: AKAP13 (16), PCDHA (11), ALDH3A2 (9), SPTAN1 (8), DOCKS (8), APP (7). Cyclophosphamide (0.35): “GENE Q”: PRLR (29), PTGER3 (23), TRPS1 (16), MUC1 (11), MED13L (9), OLFM1 (9), RABEP1 (9), GATA3-AS1 (8), MGP (8), PGR (8), ABCC6 (7), ANXA9 (7), DNALI1 (7), ELF5 (7). Dacarbazine (0.4): “GENE Q”: IKZF1 (22), PTPRC (13), LCP2 (12), CDK6 (11), CHD4 (10), PTPN22 (10), LAPTM5 (9), LRMP (8), WAS (8), MAP3K7 (8), SLA (8), ETV6 (7), ITGA4 (7), RNF138 (7); “GENE Y”: CTBP2 (13), GNA11 (9), ARHGAP35 (8), TACC2 (7). Estramustine (0.4): “GENE Q”: ELL2 (8), GAS1 (7), SURF4 (7). Gemcitabine (0.35): “GENE Y”: DLG3 (14), MYH14 (13), EHF (10), CXADR (9), TRAK1 (9), CLDN4 (8), TMEM45B (8), KLF4 (7), OVOL2 (7), SHROOM3 (7). Idarubicin (0.35): “GENE Q”: NAP1L1 (19), EIF1 (9), NSL1 (8), MPHOSPH9 (7); “GENE Y”: ABCB1 (13), CDKN2A (9), CCDC8 (8), CHKA (8), RIMS2 (8), SNRPN (8), AMOTL1 (7), ITCH (7), TSTA3 (7). Ifosfamide (0.4): “GENE Q”: BCL11A (17), TFDP2 (13), ITGA4 (9), LEF1 (9), BCL11B (8), LRMP (8), SH2D1A (8), CHRNA3 (7), NFATC3 (7), FYB (7), GIMAP6 (7), OGN (7), LDLRAD4 (7), SLIT1 (7), TOX (7); “GENE Y”: LPAR1 (7). Lomustine (0.35): “GENE 0”: RORA (17), PDE4DIP (16), KIR2DL4 (13), SOCS3 (12), ALK (10), GAS1 (10), KIR2DL1 (10), TMEM260 (10), HLA-DQB1 (9), HLA-DRB1 (9), SERPINA1 (9), SOCS1 (9), SERPINB3 (8), SERPINB4 (8), TSHZ2 (8), IRF5 (8), KIAA0226L (8), RORC (8), ACPP (7), HTRA3 (7), IL2RA (7), JAK3 (7); “GENE Y”: GNA11 (9). Melphalan (0.4): “GENE Q”: IKZF1 (22), PTPRC (16), N4BP2L1 (14), SLA (11), NAP1L1 (10), ITGA4 (9), WAS (9), ZNRF1 (9), SLC16A7 (9), ACAP1 (7), PTPN22 (7); “GENE Y”: CHKA (9), AGAP1 (8), BAIAP2L1 (8), MYO1G (7). Methotrexate (0.4): “GENE Q”: HNRNPC (16), PTBP1 (10), HNRNPD (8), RFC5 (8), SET (8), THUMPD1 (8), SART3 (7), KARS (7), METAP2 (7), MST4 (7), NUDT21 (7); “GENE Y”: NOTCH2 (13), CAP1 (7), DUSP10 (9). Mitoxantrone (0.4): “GENE Q”: NAP1L1 (22), NUCKS1 (14), ZNRF1 (9), ITGA4 (7), MPHOSPH9 (7); “GENE Y”: EXOSC4 (8), CHKA (8), TGFBR3 (8). Mustine (0.4): “GENE Q”: NAP1L1 (17), PTPRC (13), HNRNPC (10), NSL1 (10), MRPL9 (9), PSD4 (8), SLA (8), DDX23 (7), MYO1G (7); “GENE Y”: CALD1 (17), GNA11 (14), ACTN1 (10), RRBP1 (9), RBFOX2 (7), TRIOBP (7), TIMP3 (7). Procarbazine (0.3): “GENE Q”: CNTN1 (9), SLMO2 (9), WWTR1 (8), PTGDS (7). Raloxifene (0.4): “GENE Q”: NCOA3 (12), TRIM33 (12), RAD51C (10), ESR1 (9), RPS6KB1 (9), SPDEF (9), DHPS (8), HIPK1 (8), GATA3 (8), BRIP1 (7), BCL2 (7), DTWD1 (7); “GENE Y”: ANXA2 (15), RBMS2 (8). Vinorelbine (0.35): “GENE Q”: FBXO45 (8), ARPC5L (7), SERBP1 (7); “GENE Y”: NR2F2 (13), CCND1 (9), CTAG1A (9), PON2 (9), FGA (8), TGM2 (8), RAB3B (8), RASAL2 (8), FGB (8), CHST9 (7), CXCL5 (7), HCFC1R1 (7), LEPROT (7), ME3 (7), PDZK1IP1 (7). Abiraterone acetate (0.35): “GENE Y”: TRO (7), GBA3 (7), HEXIM1 (7). Capecitabine (0.3): “GENE Q”: SSX3 (16), PPFIA1 (11), ST13 (11), NRXN1 (9), RANGAP1 (9), FCRLA (9), HLA-DRB1 (9), NHP2L1 (8), SMDT1 (8), SSX2 (8), DESI1 (7), FGF13 (7), GBA3 (7), MYOZ2 (7), SSX1 (7), SSX4B (7). Darolutamide (0.4): “GENE Q”: LST1 (17), LILRA2 (12), AIF1 (10), ARHGAP9 (10), LCP2 (10), MPO (8), ARHGAP25 (7), LMO2 (7), RASGRP2 (7), WAS (7); “GENE Y”: CTNNA1 (21), ANXA2 (15), CTBP2 (13), IKZF1 (13), CMTM4 (9), TWF1 (9), BIN1 (8), CRK (8), GNA11 (8), DOCK1 (7), NCKAP1 (7), PFN2 (7), TSPAN6 (7). Venetoclax (0.35): “GENE Q”: LST1 (17), LILRA2 (15), ZADH2 (11), STK10 (10), MPO (9), IL17RA (8), KIAA0930 (8), LYST (8), NCF4 (8), PLEK (8), ME2 (8), TARP (8), AIF1 (7), ARHGAP25 (7), FRY (7), PRKCD (7), RASGRP2 (7), TFEC (7); “GENE Y”: ANXA2 (11), CRK (8), EZR (8), AMFR (7). Vindesine sulfate (0.4): “GENE Y”: SEMA6A (16), CEACAM1 (13), RHOQ (12), APOE (11), CDK2 (9), TRPM1 (9), DUSP10 (9), MBP (9), PLXNC1 (9), RGS12 (9), LDB3 (8), MMP8 (8), MCAM (8), TNS1 (8), ATP6VOC (7), PRTG (7), ITGA7 (7), MLANA (7), STX7 (7). 6-Mercaptopurine (0.4): “GENE Q”: CHD4 (9), LYAR (8), RCL1 (8), ZNRD1 (8), GART (7), HNRNPD (7), MRPL42 (7), RPL3 (7), U2AF1 (7), UBE2N (7); “GENE Y”: ATP6VOE1 (11), CHST3 (9), CLIP4 (7), EPHA4 (7), LHFP (7), ZBTB4 (7). Actinomycin D (0.4): “GENE Q”: FBXO45 (10); “GENE Y”: RBMS2 (19), ABCB1 (14), NR2F2 (13), AJUBA (10), WWC1 (10), LEPROT (10), NNMT (9), TRAM1 (9), TGM2 (9), BIN1 (8), MPP5 (8), KTN1 (8), PROSER2 (8), NXA2 (7), ITM2B (7). Azacitidine (0.35): “GENE Q”: CACYBP (9); “GENE Y”: GTF2I (10), PSEN2 (9), HS2ST1 (7), CD99 (7), FAM65B (7), PDE3A (7). Belinostat (0.4): “GENE Y”: NRXN3 (7). Busulfan (0.35): “GENE Q”: IKZF1 (21), PTPRC (16), KIR2DL1 (12), KIR2DL4 (11), SLA (11), SOCS3 (10), ALK (9), WAS (9), N4BP2L1 (8), RORA (8), TMEM260 (8), SERPINB3 (8), SERPINB4 (8), ACAP1 (7), CDK6 (7), MYO1G (7), RAB33A (7); “GENE Y”: CTDSPL (10), AGAP1 (9), KIF13A (8), KLF3 (8), YWHAZ (8). Chlorambucil (0.45): “GENE Q”: IKZF1 (22), PTPRC (16), N4BP2L1 (13), LCP2 (11), SLA (11), ITGA4 (10), ARHGAP9 (9), FNBP1 (8), PTPN22 (8), WAS (8), ACAP1 (7), AIF1 (7), CD84 (7), FMNL1 (7), MYO1G (7), ANP32E (6); “GENE Y”: AGAP1 (11), CTDSPL (10), DDR1 (10), KLF3 (10), DOCK6 (8), LMNA (7), ATP6V1A (7), BAIAP2 (7), BAIAP2L1 (7), TMBIM1 (7). Cladribine (0.35): “GENE Q”: JRK (8), SNRPN (8), NUDT21 (7), RNGTT (7); “GENE Y”: TTC3 (10), AHCYL1 (7). Clofarabine (0.35): “GENE Q”: PDS5A (9), PRPF8 (8); “GENE Y”: SPON1 (15), FGF18 (10), ITGB8 (9), DR1 (8), ERBB4 (8), FZD8 (7), SHROOM3 (7). Cytarabine (0.4): “GENE Q”: ESD (11), MPHOSPH9 (10), NAP1L1 (9), ARHGAP19 (8), CAND1 (8), KDM5A (8), MST4 (8), CCND3 (7), CTCF (7), EP400 (7), HNRNPC (7); “GENE Y”: PHACTR2 (8), PTPN21 (8). Decitabine (0.35): “GENE Q”: GK (11), PTPRC (9), RABEP1 (9), TBRG1 (8), BUB3 (7), PCM1 (7); “GENE Y”: CTNNA1 (13), BMPR2 (10), LAPTM4B (8), RAI14 (8), RRAS2 (8), CDKN1C (7), ITM2B (7), RAB1A (7). Eribulin mesilate (0.4): “GENE Q”: ATP11B (7); “GENE Y”: PRLR (24), ABCB1 (14), UGCG (14), NR2F2 (13), PLCB4 (12), PTGER3 (10), JAKI (9), RAB3B (9), LEPROT (9), ABCC6 (8), CLDN1 (8), DCAF6 (7), DNALI1 (7), MATN3 (7). Exemestane (0.35): “GENE Q”: CAPN3 (13), FCRLA (10), SSX3 (9), EDNRB (8), ST3GAL6 (8), TRPM1 (8), POLR2F (7), SLC11A2 (7). Floxuridine (0.35): “GENE Q”: NAP1L1 (11), MED13 (10), NXPE3 (9), FGF2 (8), TAOK1 (8); “GENE Y”: CSF2RA (10), CHKA (9), MPZL2 (8), SCEL (7). Fludarabine (0.35): “GENE Q”: IKZF1 (18), TFDP2 (14), BCL11A (12), CHRNA3 (9), PTPRC (9), BCL11B (8), SH2D1A (8), GIMAP6 (7), ITGA4 (7), N4BP2L1 (7), SLIT1 (7), ZNF22 (7); “GENE Y”: AGAP1 (11), ITGB5 (8), CEP170B (7), GGA2 (7). Fulvestrant (0.4): “GENE Q”: NCOA3 (15), ESR1 (13), GATA3 (11), PDCD4 (11), APPBP2 (10), HBA1 (10), RAD51C (10), RPS6KB1 (10), CYB561 (9), SCAMPI (9), SPDEF (9), TUBD1 (9), BCL2 (8), PPM1D (8), BRIP1 (7), DTWD1 (7), GSE1 (7), HEATR6 (7), HIPK1 (7), SIAH2 (7), TBC1D30 (7); “GENE Y”: BIN1 (8), ANXA2 (7), SPTBN1 (7). Hexamethylmelamine (0.3): “GENE Q”: ABCB1 (14), DOK4 (10), SNRPN (8), CDX2 (7), KIF26A (7); “GENE Y”: RTFDC1 (7). Irinotecan (0.35): “GENE Q”: NAP1L1 (16), PRPS1 (9), BCAT1 (7), DNAJC8 (7), MST4 (7), YWHAE (7); “GENE Y”: CHKA (9), BCR (8), SHROOM3 (8), BAIAP2L1 (7), LMOD1 (7). Irofulven (0.4): “GENE Q”: ANXA2 (10), CTBP2 (10), BMPR1A (7), GNA11 (7); “GENE Y”: IGKC (21), IGLC1 (19), TCF4 (18), MAF (13), FAM26F (11), CCR1 (11), CCND2 (9), FAM46C (9), IGHG1 (9), CD28 (9), CD8B (8), EPB41 (8), TNFRSF18 (7), IGLJ3 (7), IQGAP2 (7), MZB1 (7), PIM2 (7). Ixazomib citrate (0.35): “GENE Q”: OPA1 (11), HPS1 (8), SLC25A36 (8), CREBRF (7); “GENE Y”: SCAND1 (15), GCLC (11), PHF20 (10), SLC7A11 (9), SLITRK6 (9), CYP4F3 (8), SCEL (8), ALDH3A2 (7), CYP1B1 (7), ERGIC2 (7), OCIAD1 (7), PHKB (7), PTHLH (7), RBM39 (7), SLAIN2 (7). Letrozole (0.35): “GENE Q”: SSX3 (15), ASXL1 (8), SBSPON (8), SSX2 (8), GBA3 (7), RANGAP1 (7). Mitomycin C (0.35): “GENE Q”: CHD9 (7), JAM3 (7), KYNU (7), MPHOSPH9 (7), NUFIP2 (7), NXPE3 (7); “GENE Y”: KLF3 (9), SHROOM3 (8), TES (8), SLC17A5 (7). Mitotane (0.35): “GENE Q”: PRLR (28), PTGER3 (16), TRPS1 (16), LPAR1 (13), MUC1 (11), STC2 (9), BNIP3L (7), KCTD6 (7), KDM4B (7), MGP (7), PGR (7), PTGER4 (7), RABEP1 (7), SLC24A3 (7). Nelarabine (0.3): “GENE Q”: SSX3 (16), ST13 (12), FCRLA (10), SMDT1 (9), CAPN3 (8), NRXN1 (8), PPFIA1 (8), RANGAP1 (8), SSX2 (8), FGF13 (7), GBA3 (7), MYOZ2 (7), NHP2L1 (7), PEXSL (7), PFKFB4 (7), POLR2F (7), SSX1 (7), SSX4B (7); “GENE Y”: HFE (8). Pentostatin (0.3): “GENE Q”: TRAM1 (10), GNG4 (7), VCAN (7). Pralatrexate (0.4): “GENE Y”: STC2 (9), ERBB3 (8), STK3 (7). Raltitrexed (0.4): “GENE Q”: NAP1L1 (11); “GENE Y”: CHKA (8). Temozolomide (0.35): “GENE Q”: PTPRC (15), IKZF1 (14), COL4A3 (9), KIAA1551 (9), SLA (8), PTGIS (7), PTPN22 (7), STATSB (7), TMOD1 (7). Teniposide (0.4): “GENE Q”: IKZF1 (20), NAP1L1 (20), MPHOSPH9 (12), PTPRC (11), ITGA4 (9), ST8SIA4 (9), SLA (9), PTPN22 (8), SSBP2 (8), FMNL1 (7), NSL1 (7), ZNRF1 (7); “GENE Y”: ABCB1 (13), PTK2 (10), ITCH (7), KLF3 (7), TSTA3 (7). Thioguanine (0.35): “GENE Q”: LARP4B (9), RAC2 (7); “GENE Y”: SESN3 (16), GPM6A (13), SEC22B (12), TKT (12), CAPS (11), TRIM2 (9), EPHA3 (8), FBXO32 (8), PTN (8), WARS2 (8), AGPS (7), GFAP (7), IGFBP7 (7), NEDD9 (7), PDGFRA (7). Topotecan (0.35): “GENE Q”: NAP1L1 (15), HDGFRP3 (8), MPHOSPH9 (7), STRADA (7), TAOK1 (7); “GENE Y”: BAIAP2L1 (9), CHKA (9), NPAS2 (9), TES (9), TGFBR3 (7). Toremifene (0.4): “GENE Q”: PSD4 (10), GTF3A (9), TLE3 (8), BCL2 (7), FOXP2 (7), RAD51C (7); “GENE Y”: LRP6 (8), ANXA2 (8), CORO1C (7). Uracil mustard (0.4): “GENE Q”: IKZF1 (22), NAP1L1 (16), PTPRC (16), N4BP2L1 (13), SLA (11), ARHGAP9 (10), ITGA4 (10), LST1 (10), AIF1 (8), PTPN22 (8), WAS (8), MPHOSPH9 (8), ACAP1 (7), CD84 (7), MYO1G (7); “GENE Y”: KLF3 (12), BAIAP2L1 (10), AGAP1 (10), GNA11 (9), CTDSPL (7), DOCK6 (7), TMBIM1 (7). Valrubicin (0.4): “GENE Q”: IKZF1 (20), NAP1L1 (19), PTPRC (14), HNRNPC (10), ITGA4 (9), CDK6 (8), MST4 (8), ST8SIA4 (8), MPHOSPH9 (8), FMNL1 (7), RPL23A (7), SLA (7); “GENE Y”: VAMP3 (9), CTDSPL (9), RALGPS2 (9), CHKA (7). Vorinostat (0.4): “GENE Q”: CHD4 (7), GDAP1 (7); “GENE Y”: PLS3 (7). Risperidone (0.3): “GENE Q”: EHF (6), SLC6A2 (5), EGFR (5), GM2A (5).
A method of identifying a subject with cancer eligible for treatment with a drug comprising testing a biological sample(s) from the subject, wherein the subject is eligible for treatment with the drug if analysis of the sample(s) indicates/ascertains that the subject's cancer's expression of one or more of this drug's “GENE Q” is above a threshold amount (e.g. a threshold amount as defined elsewhere herein; and/or is substantially similar to that of a cancer[s] observed/known/reported to be responsive/susceptible to this drug), and/or the subject's cancer's expression of one or more of this drug's “GENE Y” is below a threshold amount (e.g. a threshold amount as defined elsewhere herein; and/or is substantially similar to that of a cancer[s]observed/known/reported to be responsive/susceptible to this drug). Optionally wherein part or all of this method is performed by at least one “companion diagnostic”, wherein the metes and bounds of this term are well understood to those of the art. Optionally wherein the companion diagnostic comprises at least one microarray.
A method of treating cancer in a subject, comprising testing a biological sample(s) from the subject to ascertain if/whether the subject's cancer's expression of one or more of a drug's “GENE Q” is above a threshold amount (e.g. a threshold amount as defined elsewhere herein; and/or is substantially similar to that of a cancer[s] observed/known/reported to be responsive/susceptible to this drug), and/or
Almitrine dimesylate's anti-cancer activity correlates with MYC (c-MYC) gene (oncogene) expression (
For many different kinds of cancer, high MYC expression/activity has been shown to correlate with drug(s) resistance and/or poor prognosis. Thence almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) is especially useful for the most dangerous/drug(s) resistant cancers. Especially because directly targeting MYC has thus far proven elusive in the clinic. Indeed, it is commonly termed “undruggable” because it is a dynamic disordered/unstructured protein lacking effective binding pockets on its surface [18]. 10058-F4 drug does disrupt MYC/Max heterodimerization but it isn't suitable for in vivo use because of fast degradation and low affinity for target [18]. And so indirect MYC targeting with the already clinical drug, almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof), is a very useful clinical approach. Componentry to this disclosure is administration of almitrine and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof to treat/ameliorate/prevent/combat cancer in a subject, wherein this cancer exhibits notable MYC activity e.g. high level of MYC mRNA and/or high MYC protein amount (optionally assayed by an immuno technique e.g. immunoprecipitation i.e. high immunopositivity).
In blood plasma, circulating DNA fragments released by cells are known as cell-free DNA (cfDNA) and can be detected by quantitative real time PCR (q-PCR), wherein this method shows that, in blood plasma, breast cancer patients have greater MYC cfDNA than healthy controls, wherein this MYC amount increases with cancer progression, thence this “liquid biopsy” method [19] is one non-limiting method to determine a subject's cancer's susceptibility (correlating with MYC amount/activity) to almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) anti-cancer treatment.
Increased MYC expression/activity is a common feature of many cancers, including many haematological cancers/leukemias/lymphomas/myelomas (e.g. reviewed in [20-21]), often correlating with poor prognosis and/or drug(s) resistance: all well known by those of the art.
CML: In CML, greater MYC expression correlates with resistance to imatinib [22]. Wherein MYC knockdown sensitizes to imatinib [23]. Wherein there is “a positive correlation between MYC expression at diagnosis and poor response to imatinib”: “MYC mRNA levels are higher in nonresponders” [24]. MYC antagonizes imatinib/dasatinib-conferred differentiation of CML [25]. shRNA against MYC, or MYC inhibitor 10058-F4, exerts anti-CML activity, wherein 10058-F4 and imatinib exert synergistic anti-cancer activity [26]. From which, almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) and imatinib/dasatinib/nilotinib/radotinib/bosutinib/ponatinib are, by the teaching of this disclosure, anticipated to exert synergistic anti-CML activity. BCR-ABL fusion oncogene acts through MYC (possibly especially important in chronic to blast transition of CML) [27]. MYC expression is significantly higher in the accelerated/blastic phase than chronic phase of CML and it is possible that increased MYC expression drives the switch out of the chronic and into the more dangerous phases of CML [28]. LYMPHOMA: Increased MYC gene expression/activity is a characteristic feature of Burkitt's lymphoma, which can be because of chromosomal translocation e.g. causing MYC to be placed downstream of the highly active immunoglobulin (Ig) promoter region, leading to overexpression of MYC [29]. MYC translocations and/or increased MYC expression/activity is involved in many other lymphomas [30], typically conferring poor prognosis, including (without restriction) diffuse large B cell lymphoma (DLBCL), B-cell lymphoma unclassifiable (BCLU), Double hit/triple hit lymphomas, “high grade B-cell lymphoma with MYC and BCL2 and/or BCL6 translocations” (a WHO category), transformed or high-grade follicular lymphoma (FL), Mantle cell lymphoma (MCL), lymphomas with plasmablastic differentiation etc, wherein MYC inhibition can treat lymphoma [31]. AML: In AML, greater MYC expression correlates with lower overall survival (OS) (for AML with myelodysplasia related changes, AML-MRC) [32]. “Significant over-expression of MYC mRNA in AML patients compared to controls” and “higher MYC was consistently associated with poorer survival” and “higher MYC-immunopositivity conferring an inferior prognosis” [33]. MYC expression is elevated in AML patient samples and cell lines [34]. Increased MYC is seen in human Acute Promyelocytic Leukemia (APL) [35]. Overexpressing MYC can actually cause AML [36-38]. “Myc expression was positively correlated with drug resistance of leukemic cells, and could act as a significant clinical biomarker for AML prognosis” wherein Myc inhibition (MYC antisense RNA or 10058-F4 drug inhibitor) can overcome drug resistance [39]. Antisense RNA to MYC can induce myeloid differentiation [40]. shRNA knockdown of MYC, or administering a drug that INdirectly targets MYC, prolongs survival of mice with AML [41]. 10058-F4 drug inhibitor of MYC causes AML cells to differentiate and, at higher concentrations, induces their apoptosis [42]. MYC is absolutely required for the development of acute hematopoietic malignancies [43]. MYC activity is involved in the danger and drug resistance of FLT3-ITD fusion protein associated AML [44]. Wherein a significant enrichment of a MYC-related gene set in FLT3-ITD compared to FLT3-WT AML samples has been observed. 98% of FLT3-ITD mutated patients had high MYC-immunopositivity. MYC activity is upregulated by the activating mutations of FLT3 receptor tyrosine kinase, found in nearly one-third of all patients with AML. Plus by RUNX1-ETO, AML1-ETO, PML/RARα, and PLZF/RARα oncogenes in AML [36, 42]. An association is observed between higher MYC-immunopositivity and mutated NPM1 alone or dual NPM1 and CEBP mutations [33]. MYC gene, located at 8924, has been found to be one of the most commonly amplified regions in AML [45-46]. So, increased MYC activity is common to all these different types of AML. ALL: In ALL: T- or B-ALL (or both) occurs in zebrafish expressing transgenic murine/human MYC controlled by a zebrafish rag2 promoter, which is active in immature B and T lymphoblasts [47]. MYC suppression by small hairpin RNA prevents T-ALL initiation in a mouse model of T-ALL [488]. Overexpression of MYC in certain ALL cell lines, or primary patient samples, correlates with aberrantly prolonged MYC half-life due to abnormalities in the phosphorylation/dephosphorylation regulating MYC stabilization/degradation [49]. T-ALL is associated with elevated MYC expression driven by NOTCH1 wherein NOTCH1 inhibition decreases MYC mRNA levels and inhibits leukemic cell growth [50, 51, 52, 53, 54, 55]. MYELOMA: In myeloma, MYC is often overexpressed at the transcription [56-57] and/or translation level [58], wherein MYC overexpression is a marker for a more aggressive myeloma (such as plasma cell leukemia [59]) and poor prognosis [60]. Indeed MYC can be causal to pre-cancer becoming myeloma [61, 62, 63]. Wherein MYC inhibition/downregulation can kill myeloma cells [64, 65, 66, 67].
Cancer susceptibility to almitrine dimesylate correlates with USP36 gene expression (with microarray data, across many different data sets, but not with RNA-seq data: correlation (R) between RNA-seq composite gene expression (log 2[FPKM+1]) and mean[1-|5-]dose(10 μM)=0.09), wherein USP36 deubiquitinates and stabilizes MYC [68]. Cancer susceptibility to almitrine dimesylate correlates with (MYC driven) HNRNPA1 gene expression, wherein HNRNPA1 is a drive to alternative splicing of pyruvate kinase, favouring the embryonic pyruvate kinase isoform, PKM2, which is a metabolic switch into aerobic glycolysis/Warburg effect [69]. Thence cancer susceptibility to almitrine dimesylate correlates with PKM2 protein amount and high PKM2/PKM1 protein ratio. Cancer susceptibility to almitrine dimesylate correlates with (MYC driven [70]) PROM1 gene (coding for CD133 protein) expression, wherein CD133 is a cancer stem cell marker (expressed on cell surface and so very amenable to immuno assays to detect its amount), associated with chemoresistance and poor prognosis [71], e.g. with acute leukemia [72]. Cancer susceptibility to almitrine dimesylate correlates with SLC38A1, sodium-coupled neutral amino acid transporter 1, which transports glutamine, wherein high expression of SLC38A1 predicts poor prognosis with many cancers, including AML [73]. A disclosure embodiment(s) is to administer almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof) to a subject that has, or that is suspected to have, or might have, a cancer(s) that favours glutaminolysis (low blood [glutamine] can be a sign of this), optionally wherein the subject adheres to a low glutamine and/or low protein diet.
That cancer susceptibility to almitrine dimesylate correlates with MYC, USP36, HNRNPA1, PROM1 (CD133 protein) and SLC38A1 expression suggests that the most dangerous, chemoresistant cancers are not least, but actually most, susceptible to the anti-cancer activity of almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof), which makes almitrine an incredibly valuable addition to the anti-cancer drug armoury.
Cancer susceptibility to almitrine dimesylate positively correlates with expression of PTGS1, wherein high PTGS1 expression characterizes imatinib resistant, from susceptible, CML [74].
Cancer susceptibility to almitrine dimesylate correlates with the expression of the following genes, which have been reported to correlate with cancer danger in one or more cancer types: CAMKK2 [75], CBLL1 [76], DNAJC12 [77], FABP6 [78], ILF2 [79], NADSYN1 [80], NPTX2 [81], RNF144B [82], TMPRSS3 [83], ZNF277 [84], TPK1 [85], ANXA11 [86] and MLL2 (also known as KMT2D) [87], wherein MLL2 is especially important to MLL1-fusion protein driven AML [88].
Square (▪) and round (●) data points relate to gene expression determined by Human Genome microarray chips made by the Affymetrix and Aligent companies respectively (for which the x-axis is mean log 2[intensity]). These two companies make microarray chips that differ in many design features e.g. probe length (25- vs. 60-mer). Triangular (▴) data points relate to gene expression determined by RNA-seq (for which the x-axis is log 2[FPKM+1], where FPKM is “Fragments Per Kilobase per Million reads”; “composite” data used {defined in main text}, rather than of any individual isoform). The concordance between the data from these two different methodologies is extremely high, which is validating.
Unadjusted, the Agilent data is much lower in value than the Affymetrix data. To make these closer in value, for ease of graphing and analysis: independently for each gene in question: the median expression of the gene across all the cancer cell lines was calculated for Affymetrix and Agilent data. The difference between these medians was then added to each Agilent gene expression data point for that gene, for every cancer cell line.
Unadjusted, the RNA-seq data is much lower in value than the Affymetrix microarray data. So, the same method was performed for the RNA-seq data. As aforementioned for Agilent data. To make all the data values closer to those of the Affymetrix microarray data. For ease of graphing and analysis.
Each Affymetrix data point is the mean of 4 different microarray data sets, sourced using 4 different Affymetrix Human Genome (HG) microarray chip sets: (1) GSE5720 data set sourced using the Affymetrix Human Genome U133A and U133B chips; (2) GSE32474 data set sourced using the Affymetrix Human Genome U133 Plus 2.0 chip; (3) GSE29682 data set sourced using the Affymetrix Human Exon 1.0 ST chip; (4) GSE5949 data set sourced using Affymetrix Human Genome U95A, U95B, U95C, U95D, U95E chips. The aforementioned GSE29682 data set has 4 probes per exon on average, and thence the number of probes for any given gene tends to scale with the number of exons it has, where the average gene has 10 exons and so there are, on average, 40 probes per gene on this chip. Focusing on just the GSE29682 data set alone, the chance that all these different probes for a single gene would correlate at sizable magnitude, all in the same direction, making the mean correlation sizeable in one direction, if there is no true correlation for that gene, is small.
The presented p-values in this figure are small. But they would probably be smaller still if raw, instead of mean, data was used. For each, this would increase n, R would likely still hold at sizable amplitude, decreasing p. Firstly, the y-axis is a mean of two datas, which could be used separately, doubling parameter n. Plus, the Agilent microarray data in this figure is a mean of underlying data in some cases, which could be utilized separately instead, increasing n. Furthermore, each Affymetrix microarray gene expression value for a cell line is a mean of multiple underlying mean data points, from 4 different independently generated (separated in time and space: generated at different times by different groups) microarray data sets, which could be used separately, increasing parameter n greatly. However, for expediency, means were utilized instead. With which there is still a sizeable value of n. Such that, given that each correlation R is high, and so p is very low in every case (as shown in the figure), there is high statistical significance. Some p values that are especially low are bolded for emphasis.
The fact that these correlations are observed, at significant amplitude, in 5 independently generated microarray data sets, sourced using multiple different microarray chips from two different companies, and 1 RNA-seq data set (so a completely different methodology), shows these correlations to be extremely robust and reliable. Moreover these same correlations are observed manifold in further microarray data sets, componentry to the NCI COMPARE [67] rather than the CellMiner [3-4] database, which weren't used to generate this figure. But for which data is presented in the main text.
Note, “mean(b, DNAJC12, TMPRSS3, PTGS1, CASP2, ZNF766, ZNF277, TPK1)” refers to (b+DNAJC12+TMPRSS3+PTGS1+CASP2+ZNF766+ZNF277+TPK1)/8, where b=(SCAF11−RAPH1), where gene name refers to the expression value of said gene.
Upper panel shows all the data: Affymetrix and Agilent microarray data, and RNA-seq data. Lower panel omits the Agilent microarray data, which increases the value of R, although there is a decrease in p because of less data points. One can see in the upper panel that the Agilent data (●) has a slightly shallower gradient than both the Affymetrix and RNA-seq data, thence why its omission (lower panel) increases R. In the lower panel, where “microarray data” is referred to, this is only Affymetrix data.
DNAJC12 gene expression data wasn't available in the Agilent microarray data set. So, it wasn't included in its calculation of Z. This is perhaps a contributing factor to the Agilent data having a shallower gradient.
For the Affymetrix microarray data in this sub-figure (only), but not for its Agilent microarray or RNA-seq data, the data point corresponding to the SF-539 cancer cell line is omitted because, when plotted, it is an outlier (not shown). Probably because, distinctly, as can be viewed in the CellMiner database, this cell line is missing from many of the underlying Affymetrix microarray data sets. Thus its mean, taken separately for each gene, is very ill constrained by data, and can more easily be thrown by a wayward data point(s). There is less to average. Thence the power of averaging is diminished. So, the mean data for this cell line is not as robust as the mean data for the other cell lines. So, on this basis alone it should be omitted. Moreover because it is an outlier when plotted. And furthermore, because it has so much less microarray data present than the other cell lines that it has no microarray data at all for one of the genes of interest shown: ZNF766. Note that accordingly, SF-539 was also omitted when calculating the median for calculating the aforementioned independent adjustment/proportionality factors for the Agilent microarray and RNA-seq data in this sub-figure. Interestingly, for the lower panel, without this omission of SF-539 (not shown): R=0.7441, n=116, p=1.06664E-21. So, still very significant.
Different and/or more and/or less gene expressions that correlate, or inversely correlate, with almitrine dimesylate anti-cancer activity (examples of which are disclosed in the main text herein) can be used to make an alternative formula to that shown, but which still conveys predictive power, which can be greater, for how susceptible a cancer is to almitrine dimesylate anti-cancer activity. For non-limiting example, an alternative formula is mean(d, DNAJC12, TMPRSS3, PTGS1, MYC, CBLL1, CASP2, SORL1, SYT1, SUPV3L1, ZNF766, ZNF277, ILF2, HMX1, FABP6, KCNC3, TPK1, CAMKK2, DDX49, LETMD1), where b=(SCAF11-d), where d=mean(ITGB5, RAPH1, RAB23), where gene name refers to the expression value of said gene. For which, only using Affymetrix microarray data (a limitation, but quicker to calculate), and with the SF-539 cell line omitted: R=0.7969=0.8, n=57, p=1.21731E-13 (adding Agilent and RNA-seq data would increase n, whilst R would likely largely hold, decreasing p further).
In disclosure embodiments, almitrine and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof is administered to, or taken by, a subject, optionally co-administered/taken with a FDA/EMA/MHRA/PMDA/NMPA approved drug(s) (optionally approved for cancer(s)/blood cancer(s), optionally an antileukimic drug(s) such as one or more of imatinib (imatinib mesylate), dasatinib, nilotinib, radotinib, bosutinib, ponatinib, and/or a drug(s) for myeloma such as thalidomide and/or thalidomide analogue(s), optionally incorporated in the same pharmaceutical composition; optionally in co-therapy with radiotherapy, optionally wherein almitrine makes the cancer more radiosensitive/less radioresistant and/or the anti-cancer activities of almitrine and radiotherapy add/synergize, and/or optionally in co-therapy with one or more chemotherapies, optionally wherein almitrine makes the cancer more chemosensitive/less chemoresistant and/or the anti-cancer activities of almitrine and chemotherap[y/ies] add/synergize, and/or the subject undergoes a bone marrow/stem cell transplant), to treat/ameliorate/prevent/combat their hematological/hematopoietic and/or lymphoid cell cancer(s)/immunoproliferative disease(s)/lymphoproliferative disorder(s)/blood cancer(s), and/or cancer(s) of bone marrow and/or lymph system (including their pre-cancers e.g. pre-leukemia), including, without restriction and including all sub-types, one or more of leukemia, lymphoma, myeloma (multiple myeloma)/plasmacytoma/Plasma cell leukemia (PCL), wherein (without restriction) all sub-types under the WHO/French-American-British (FAB)/European Leukaemia Net (ELN) classification system are contemplated, wherein lymphoma (to illustrate and not restrict) can be Hodgkin's Lymphoma (HL), Non-Hodgkin's lymphoma (NHL), an HIV associated lymphoma and/or Epstein-Barr virus-associated lymphoproliferative disease, wherein leukemia can be (to illustrate and not restrict) Acute Leukemia, Chronic Leukemia, or to be more specific: Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML) {including blast crisis of CML} or a leukemia not necessarily fitting into one of these 4 classifications such as Hairy cell Leukemia (HCL), Hairy Cell Leukemia-Variant (HCL-V), Hairy Cell Leukemia-Japanese Variant (HCL-J), Mixed-Phenotype Acute Leukemia (MPAL, biphenotypic leukaemia, acute biphenotypic ALL, mixture of AML/ALL, the abnormal cells have protein markers for both ALL and AML), aggressive NK-cell leukemia, juvenile myelomonocytic leukemia, large granular lymphocytic leukemia, B cell prolymphocytic leukemia (B-PLL), T-cell prolymphocytic leukemia (T-PLL), Adult T-cell leukemia/lymphoma, T-lymphoblastic leukemia/lymphoma, T-cell acute lymphoblastic leukemia, chronic eosinophilic leukemia (CEL), clonal eosinophilias (may be pre-cancerous or cancerous). Wherein (without restriction), ALL includes precursor B acute lymphoblastic leukemia (B-cell ALL), precursor T acute lymphoblastic leukemia (T-cell ALL), Burkitt's leukemia, Philadelphia chromosome positive ALL, AML includes acute promyelocytic leukemia, acute myeloblastic leukemia, acute megakaryoblastic leukemia, acute erythroid leukemia, acute monocytic leukemia, Acute Myeloid Leukemia with Myelodysplasia Related Changes (AML-MRC), CLL includes B-cell prolymphocytic leukemia, CML includes chronic myelomonocytic leukemia, blast crisis of CML, Philadelphia chromosome positive CML. Wherein (illustrating without restriction) Hodgkin's lymphoma can be classical Hodgkin's lymphoma (nodular sclerosing HL, mixed-cellularity subtype, lymphocyte-rich, lymphocyte depleted), nodular lymphocyte-predominant Hodgkin lymphoma. Non-Hodgkin's lymphoma (NHL) can be (to illustrate and not restrict) follicular lymphoma, primary cutaneous follicular lymphoma, primary cutaneous marginal zone lymphoma (primary cutaneous immunocytoma, marginal zone B-cell lymphoma, mucosa-associated lymphoid tissue lymphoma), intravascular large B-cell lymphoma, Burkitt's lymphoma, Burkitt's-like lymphoma, cutaneous T cell lymphoma, T-cell lymphoma, B-cell lymphoma, diffuse large B cell lymphoma (including primary mediastinal B-cell lymphoma, primary mediastinal (thymic) large B cell lymphoma, Mantle cell lymphoma, germinal center B-cell like diffuse large B-cell lymphoma). Epstein-Barr virus-associated lymphoproliferative disease can be (without restriction) Epstein-Barr virus-positive Hodgkin lymphoma, Epstein-Barr virus-positive (EBV+) diffuse large B cell lymphoma, Epstein-Barr virus-associated diffuse large B cell lymphoma associated with chronic inflammation, Epstein-Barr virus-positive Burkitt lymphoma, Epstein-Barr virus-positive lymphomatoid granulomatosis, Extranodal NK/T-cell lymphoma nasal type, Epstein-Barr virus-associated plasma cell myeloma. Also contemplated by this disclosure is follicular dendritic cell sarcoma (similarities in presentation and markers to lymphoma), Waldenström's macroglobulinemia (WM, type of cancer affecting two types of B cells: lymphoplasmacytoid cells and plasma cells) and Richter's syndrome (RS, wherein CLL or hairy cell leukemia transforms into a Hodgkin or non-Hodgkin lymphoma). Pre-leukemia includes (without restriction) monoclonal B-cell lymphocytosis, transient myeloproliferative disease, myelodysplastic syndrome (MDS), myeloproliferative neoplasm (MPN), myelodysplastic-myeloproliferative disease, lymphoproliferative disorder. Any cancer or pre-cancer mentioned herein is especially contemplated for treatment with almitrine (and/or a pharmaceutically-acceptable salt, solvate, hydrate or prodrug thereof).
This disclosure encompasses all combinations of aspects of the disclosure noted herein. It is understood that any and all embodiments of the present disclosure may be taken in conjunction with any other embodiment or embodiments to describe additional embodiments. It is also to be understood that each individual element of the embodiments is its own independent embodiment. Furthermore, any element of an embodiment is meant to be combined with any and all other elements from any embodiment to describe an additional embodiment. Feature(s) described in connection with one embodiment of the disclosure may be used in conjunction with another embodiment(s), even if not explicitly stated. Any/all of the features described herein (including any accompanying claims, abstract and drawings), and/or all/some of the steps of any method or process so disclosed, may be combined with any of the above aspects in any combination.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2021/058639 | 9/22/2021 | WO |
