extfieldsofstudy: Biology Medicine

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Combining Mutational Signatures, Clonal Fitness, and Drug Affinity to Define Drug-Specific Resistance Mutations in Cancer

Summary The emergence of mutations that confer resistance to molecularly targeted therapeutics is dependent upon the effect of each mutation on drug affinity for the target protein, the clonal fitness of cells harboring the mutation, and the probability that each variant can be generated by DNA codon base mutation. We present a computational workflow that combines these three factors to identify mutations likely to arise upon drug treatment in a particular tumor type. The Osprey-based workflow is validated using a comprehensive dataset of ERK2 mutations and is applied to small-molecule drugs and/or therapeutic antibodies targeting KIT, EGFR, Abl, and ALK. We identify major clinically observed drug-resistant mutations for drug-target pairs and highlight the potential to prospectively identify probable drug resistance mutations.

INTRODUCTION
Although targeted cancer therapies, for example, against kinases, hormone receptors, or hormone-synthesizing enzymes, have shown clinical success, many patients develop resistance to treatment and subsequently relapse. Second-and third-generation drugs are being developed to target these resistant mutants; however, there is a significant time span between the detection of clinically validated resistance mutations and the availability of suitably targeted treatment options. Early identification of drug-specific mutations is therefore critical and the aim of this study.
Several mechanisms underlying resistance to targeted drugs have been described (Holohan et al., 2013), including mutations directly affecting the drug target. Such mutations may, for example, increase affinity for the endogenous co-factor ATP, thereby decreasing the relative affinity of an ATP-competitive drug. Mutations within the binding site may also alter drugprotein interactions and directly interfere with drug binding (Barouch-Bentov and Sauer, 2011).
Several computational studies have investigated the impact of protein mutations on drug efficacy (Kamasani et al., 2017;Mar-tínez-Jimé nez et al., 2017;Paul et al., 2016;Reeve et al., 2015), mainly in an antiviral or antibacterial context (Frey et al., 2010;Hosseini et al., 2016;Fowler et al., 2018). However, rarely do these studies prospectively identify drug resistance mutations (Frey et al., 2010;Paul et al., 2016;Reeve et al., 2015). In predicting mutations that render Staphylococcus aureus resistant to an antifolate antibiotic, Reeve et al. (2015) evaluated the likely effect of possible mutations on both binding of the inhibitor and on binding of the endogenous ligand -an important aspect since any mutation that significantly abrogates the native activity of the wild-type (WT) protein is unlikely to survive selective evolutionary pressure (Gil and Rodriguez, 2016;Sprouffske et al., 2012;Pandurangan et al., 2017). However, Reeve et al. do not consider the likelihood of whether each mutation can be formed in bacteria.
In cancer, the mutation landscape of a tumor can be characterized by the mutational signatures operating in a particular cancer type (Alexandrov et al., 2013). These signatures describe the probability of a specific base exchange within a defined trinucleotide context. Some of these signatures have been associated with known mutagenic processes, such as UV irradiation or aging, while the mechanism of others still remains elusive (Alexandrov et al., 2013). These mutagenic processes can generate a single clone harboring the disease-causing ''driver mutation,'' which ultimately leads to the development of cancer (Greaves and Maley, 2012). In addition, non-transforming somatic mutations, so-called passenger mutations, are randomly created. While not oncogenic per se, passenger mutations can provide the substrate for an evolutionary advantage throughout cancer progression, for example, under the selective pressure of a targeted molecular therapy, leading to drug resistance. Known drug resistance mutations have not only been detected in treatment-naive patients (Inukai et al., 2006;Roche-Lestienne et al., 2002), but also in healthy individuals (Gurden et al., 2015). This suggests that small pools of viable treatment-resistant clones can pre-exist in patients and that drug treatment puts a selection pressure on a heterogeneous cancer cell population that selects for resistant sub-clones.
Each drug interacts with its biological target in a unique way, and each protein target mutation will differentially affect diverse classes of drugs. As a consequence, each compound can be expected to exhibit a unique resistance mutation profile. Three factors contribute to the probability and functional impact of a residue change: (1) the probability that the protein mutation can be generated from a DNA mutational signature (signature-driven probability), (2) whether the mutation maintains protein function and clones harboring the mutation are still viable (fitness), and (3) whether the mutation confers lower drug affinity with respect to the endogenous ligand for the target protein (affinity). Martínez-Jimé nez et al. (2017) recently reported a workflow classi-fying potential drug resistance mutations based on Random Forest models and mutation signatures. However, the effect of mutations on the fitness of the clone was not taken into account. In addition, only single-point mutations (SPMs) were considered, despite the notable detection of double-point mutations (DPMs) in cancer patients (Table S1).
We report an in silico cascade that sequentially evaluates the probability of generating any mutant within 5 Å of a bound ligand, the clonal fitness of each mutation, and the effect of each mutation on drug affinity in order to systematically and objectively prioritize mutations that are highly likely to arise under drug treatment. Importantly, our workflow classifies the impact of a mutation on drug affinity relative to endogenous ligand and does not rely upon accurate calculation of binding free energies; it also ranks mutations according to their likelihood of being generated in particular cancer type. The workflow ( Figure 1 and described in detail below) is validated on a comprehensive benchmark dataset that describes the effect of nearly all possible extracellular signal-regulated kinase 2 (ERK2) missense mutations on sensitivity to the ERK2 inhibitor SCH772984 (Brenan et al., 2016). In addition, we apply the workflow to four well-established cancer targets and evaluate at least two US Food and Drug Administration-approved drugs (small molecules or biologics) per target.

Affinity
In the first step, the software package Osprey Gainza et al., 2013) was applied to evaluate the potential impact of protein active site mutations on both drug and endogenous ligand binding using protein crystallographic data.
Osprey uses ensembles of minimized side chain rotamers to calculate a K* score Gainza et al., 2013), which estimates ligand binding to the evaluated protein construct. Importantly, by employing ensembles of rotamers, Osprey takes the dynamic nature of protein amino acid side chains into account. K* scores were computed for the WT protein and all possible amino acid exchanges for both the drug and the endogenous ligand-protein complex with the exception of Pro, which cannot be processed by Osprey. We identify mutants that reduce affinity for the drug in comparison with the endogenous ligand and are therefore more likely to drive a viable resistant clone. Importantly, calculation of absolute drug affinity for WT and mutant proteins is not required. Please refer to the STAR Methods for additional details.

Fitness
Clones harboring a mutant oncoprotein that is unable to bind its endogenous ligand lose their function and are unlikely to persist. All such deleterious mutants with a K* score of 0.0 for the endogenous ligand were discarded.

Exclusion of Triple Point Mutations
Base codons for each WT residue were retrieved from the COSMIC database . Amino acid exchanges that would require mutation of all three codon bases were discarded. We hypothesized that three contiguous coding Potential mutations are evaluated based on their predicted effect on the affinity of both the drug and endogenous ligand (orange), the fitness of the resultant clone (blue), and the requirement for triple-point mutations to generate a mutant (lime green). Resistance hotspots are identified within the remaining set of resistant mutants; these resistance hotspots are protein residues where multiple amino acid changes are predicted to lead to resistance and which therefore have a high likelihood of functional relevance. Resistant mutations at these hotspots are prioritized based on the probability that they will be generated according to the known DNA mutational signatures operating in a particular cancer type. base mutations are highly unlikely and, to the best of our knowledge, no such patient case has been reported. While the majority of drug resistance mutations arise from SPMs, we also included DPMs (so-called tandem mutations), which represent 5% (7 of 132) of unique drug resistance mutations with defined genetic alterations in the COSMIC database (access date 5 January 2018) and occur with similar frequency to insertions (Table S1).
Resistance Hotspots and Signature-Driven Probability Functional protein mutants were further triaged according to the number of predicted mutations at a specific residue position. This approach identifies resistance hotspots, where multiple different viable mutants are predicted to cause resistance to the drug under study. While comparing the prevalence of mutations at hotspots in a particular drug-target pair can be used for ranking positions, this method cannot be applied to crosscompare the prevalence of mutations at hotspots between drug-target pairs to infer global inter-target susceptibility to resistance. The top three ranked resistance hotspots were then analyzed in more detail in order to further prioritize amino acid changes at these positions that have a high probability (relP) of being generated in particular tumor types according to the DNA mutational signatures reported by Alexandrov et al. (2013). Each possible resistance hotspot mutation was ranked according to its derived relP and the top-ranked amino acid changes were considered most relevant. Please refer to the STAR Methods for additional details.

ERK2: A Benchmarking Study
In 2016, Brenan and colleagues systematically studied the effect of a wide range of ERK2 genetic alterations on the cellular response to the ERK2 inhibitor SCH772984 (Brenan et al., 2016). They generated nearly every possible missense mutation using multiplexed site-directed mutagenesis. Pooled vectors were used to express ERK2 mutants in A375 human malignant melanoma cells; after drug exposure, the enrichment of resistant variants relative to their abundance in the initial cell population was determined by massively parallel sequencing.
This comprehensive dataset of possible resistant mutants to the ERK2 inhibitor SCH772984 provides a benchmark to evaluate the performance of our workflow. However, as a consequence of the aggressive mutant-generation protocol, this dataset includes protein mutants that require exchange of all three coding bases. Such mutants are excluded in our workflow (see above) and were also removed from the Brenan dataset.
Brenan and colleagues reported 46 experimentally observed resistance mutations within 5 Å of the bound ligand that were not mutations to Pro or triple-point mutations. For SCH772984, 559 mutants covering 31 residues were evaluated by our workflow. The number of true-positive (TP) (mutants predicted to confer resistance that were confirmed by experimental testing), true-negative (mutants predicted to be sensitive that did not confer resistance in experimental testing), false-positive (FP) (mutants predicted to confer resistance that were found to be sensitive in experimental testing), and false-negative (mutants predicted to be sensitive, but conferred resistance in the experimental testing) predictions was calculated ( Figure 2A).
Our workflow correctly classifies 84.6% of mutants and identifies almost 40% of the experimentally observed resistance mu-tations reported by Brenan et al. When considering only residue positions, 80% of experimentally observed mutated residues are discovered. The majority of experimentally evaluated ERK2 mutants remained sensitive to SCH772984 treatment and are correctly identified as sensitive by our approach, contributing to the high overall proportion (84.6%) of correct predictions. The low TP rate for SCH772984 (3.2% of mutants predicted to confer resistance were confirmed by experimental testing) is consistent with the experimental observation that only a small proportion of possible mutants (8.2%) cause drug resistance. The prioritized hotspot residues and their corresponding reported resistance mutations are provided in Table 1. For a detailed list of all predicted mutations please refer to Table S2.

Clinical Case Studies
We studied four protein kinases (KIT, epidermal growth factor receptor [EGFR], breakpoint cluster region-Abelson [Bcr-Abl] kinase, and anaplastic lymphoma kinase [ALK]). In each case protein-ligand crystal structures of approved drugs exemplify multiple generations of compound design; furthermore, as inhibitors of these targets provide the current standard of care for multiple cancer types, clinically observed resistance mutations are documented for most compounds and provide the potential for clinical validation of our computational workflow.

KIT
The first-generation inhibitor imatinib is resistant to the KIT gatekeeper mutation T670I (Antonescu et al., 2005;Tamborini et al., 2004). We investigated 648 possible mutations, all of which lie within 5 Å of either imatinib or ADP; 68 mutations remained after filtering for reduced drug binding, fitness of the clones, and triple-point mutants. For residue T670, eight mutations are predicted to cause resistance to imatinib, making it the first-ranked resistance hotspot; within this set, gatekeeper mutation T670I has the highest relP. Furthermore, the known resistance mutation T670E (Wardelmann et al., 2005) is ranked sixth by relP. Positions C809 and V668 are identified as second-and thirdranked resistance hotspots, respectively. C809R, the mutation with the highest relP, has been reported in a patient diagnosed with myelodysplastic syndrome-derived leukemia (Lorenzo et al., 2006), highlighting its biological relevance; however, similar to all predicted V668 mutations, this residue has not yet been associated with imatinib resistance.
The second-generation inhibitor sunitinib is reported to overcome resistance to the gatekeeper mutation T670I and, consistent with these reports, T670I/E mutations are not predicted to elicit sunitinib resistance by our workflow. Indeed, none of our predicted resistance mutations to sunitinib has yet been associated with clinically observed resistance. Only one resistance mutation has so far been reported for sunitinib in the COSMIC database , access date 23 November 2017); this mutation is not within the ATP-binding site.
Ponatinib has been shown to bind KIT, but is not approved for KIT-associated cancer types and clinical data are therefore lacking. Our method predicts resistance to ponatinib due to less-favorable interactions with the gatekeeper T607I mutation; however, in vitro data suggest that this mutant is sensitive to ponatinib treatment (Garner et al., 2014). Prioritized KIT resistance hotspot residues and their corresponding reported clinical resistance mutations are provided in Table 2. A full list of predicted resistance mutations and their relP values is available in Table S3.

EGFR
Applying our workflow to the first-generation EGFR inhibitor erlotinib revealed that G796 is the resistance hotspot with the most predicted mutations. Consistent with this prediction, the G796R mutation with the second-ranked relP, has been reported to weaken the affinity for erlotinib in comparison with the WT enzyme (Avizienyte et al., 2008). T790 is the second-ranked resistance hotspot with gatekeeper mutation T790M (Pao et al., 2005) having the second highest relP. L718, L788, and T854 share the third-ranked hotspot, with three predicted (B) Predicted resistance hotspots that are consistent with clinically observed resistant mutants for the representative case study EGFR (gray) and osimertinib (orange), PDB: 4ZAU (Yosaatmadja et al., 2015). Residues within 5 Å of the ligand are depicted in gray, predicted and clinically observed resistance hotspots are highlighted as crimson sticks and labeled, predicted hotspot residues that have not yet been observed in the clinic are shown as pink sticks; figure created with PyMOL (PyMOL Molecular Graphics System, Version 1.7, Schrö dinger, LLC). (C) Contribution of filtering steps to the identification of resistance mutations. The majority of mutants were discarded because they did not decrease drug affinity in comparison with binding of the endogenous ligand (orange). Mutations were further removed because of abrogated clonal fitness (blue) or because they required triple codon changes to be formed (green). The remaining pool of mutations (crimson) is predicted likely to confer resistance to drug treatment. mutations each. L788F has been detected in lung adenocarcinoma patient samples highlighting its clinical relevance (Liu et al., 2013); however, similar to all other third-rank mutations, L788F has not yet been associated with clinical resistance to erlotinib. Gefitinib, another first-generation EGFR inhibitor, elicits a similar predicted resistant mutation profile to erlotinib (Table 3).
The third-generation inhibitor osimertinib binds reversibly to EGFR prior to covalent bond formation (Yosaatmadja et al., 2015). Osimertinib was reported to overcome resistance to T790M and, consistent with this finding, the T790M mutation is not predicted to elicit resistance to osimertinib according to our protocol. Residues G796 (14 mutations), C797 (4 mutations), L718, G719, V726, and A743 (3 mutations each) are the topranked hotspot residues predicted to confer resistance. During the preparation of this manuscript, osimertinib resistance mutations affecting C797 (Ou et al., 2017b), G796 (Ou et al., 2017b;Zheng et al., 2017), and L718 (Ou et al., 2017a) (Figure 2B) were reported in clinical studies, building confidence in the prospective utility of our approach.
Further to small-molecule EGFR inhibitors, anti-EGFR monoclonal antibodies cetuximab and panitumumab are approved for the treatment of metastatic colorectal cancer. In contrast to small-molecule ATP-competitive kinase inhibitors, cetuximab and panitumumab interact with the extracellular domain of EGFR.
We evaluated the effect of extracellular domain mutations on antibody affinity and affinity of the endogenous ligands EGF and transforming growth factor a (TGF-a)--interactions that have been characterized by protein structural data. Both EGF and TGF-a are peptidic macromolecules with larger interaction surfaces compared with small molecules; thus evaluation of a higher number of possible mutations was necessary to ensure coverage of the extensive protein-protein interaction interface (1,153 and 1,207 mutations for cetuximab and panitumumab, respectively).
For both cetuximab and panitumumab, resistance mutation G441R (residue G465R in the mature protein sequence including the signaling peptide [mps]) has been detected in the clinic (Braig et al., 2015). G441 is the second-ranked resistance hotspot, with mutation G441R ranked first according to relP for both antibodies (Table 3). Confirmed clinical resistance mutations to cetuximab have also been identified at residues S468 (S468R [S492R in the mps]) (Montagut et al., 2012) and I467 (Arena et al., 2015) (I467M [I491M in the mps]); the fourth-and fifthranked resistance hotspots in our workflow (Table S4).
Our top-ranked predicted hotspot residues, S418 and G471 have not yet been associated with clinical resistance to cetuximab or panitumumab; however, seven endogenous ligands can activate EGFR (Schneider and Wolf, 2009) of which only EGF and TGF-a interactions are characterized by protein structural data. Residues S418 and G471, while not predicted to affect the interaction of EGFR with endogenous ligands EGF and TGF-a, may be important for interaction with one or more of the remaining five endogenous ligands such that mutation at these residues reduces clonal fitness and persistence. For example, we predict that several mutations affecting EGFR residues S440 and V417 tolerate TGF-a binding, but abrogate EGF binding, and are therefore filtered out in our workflow. Given the lack of structural data for the majority of endogenous EGFR ligands, and the number of potential mutations, the identification of clinically relevant resistance mutations is encouraging. All prioritized EGFR hotspot residues and corresponding reported clinical resistance mutations are summarized in Table 3. A list of all predicted mutations and relPs is provided in Table S4.

Abl
The hotspot residue with the highest number of predicted mutants for the Abl inhibitor imatinib is A380, for which clinically observed occurrences have yet to be reported. The secondrank predicted hotspot is shared by residues V256, A269, and T315. For V256 and T315, resistance mutations V256L Tiribelli et al., 2013), gatekeeper mutation T315I (Gorre et al., 2001), and T315V (Redaelli et al., 2012) have all been identified in the clinic ( Table 4). The contribution of mutational signatures to the landscape of mutations has not yet been reported for chronic myeloid leukemia (CML), and therefore the relP for hotspot mutations predicted for Abl residues could not be calculated. We obtained similar and consistent predictions for nilotinib, dasatinib, and bosutinib (Table 4). Additional resistance mutations affecting L248 are predicted for dasatinib consistent with in vitro data (Redaelli et al., 2012). While these mutations have been observed in the clinic (Redaelli et al., 2012), they have not yet been reported as resistant mutants for patients treated with dasatinib. Similarly, the effect of the predicted V256L resistant mutation on nilotinib activity has not been reported.
Axitinib has been reported to overcome resistance to the T315I gatekeeper mutation of Abl (Pemovska et al., 2015). Although not ranked among the top three resistance hotspots, axitinib is predicted to be resistant to T315I. Crystallographic studies of the T315I mutation in complex with axitinib revealed that this mutation causes large conformational changes of Abl compared with WT protein (Pemovska et al., 2015), which may not be adequately captured by our method. The L248R mutation, reported to confer resistance to axatinib in vitro (Pemovska et al., 2015), is prioritized by our workflow; however, clinical resistance data for axitinib targeting Abl is not yet available. A summary of the prioritized Abl hotspot residues and their corresponding reported clinical resistance mutations are provided in Table 4. A full list of predicted resistance mutations is provided in Table S5.

ALK
The hotspot residue with the highest frequency of predicted mutations for the first-generation ALK inhibitor crizotinib is G1269 (13 mutants). The clinically observed resistance mutation G1269A (Doebele et al., 2012;Gainor et al., 2016) and G1269S and G1269C mutations (Zhang et al., 2011), which confer resistance in vitro, rank at positions four, seven, and eight, respectively, based upon their relP values. For the second-ranked resistance hotspot G1202, 12 mutants were predicted and the known resistance mutation G1202R (Gainor et al., 2016;Katayama et al., 2012) has the second highest relP. Hotspots I1122, G1201, and D1203 rank third equal. While no G1201 mutations are associated with resistance, the D1203N mutation confers resistance in vitro (Gainor et al., 2016;Heuckmann et al., 2011) and has been associated with resistance in patients (Zhang et al., 2016). I1122V has been detected in a resistance screen against the second-generation inhibitor brigatinib, and has also T670I was predicted to confer resistance to ponatinib; however, T670I is reported to be sensitive to ponatinib (Garner et al., 2014). f NA, not applicable, tied resistance hotspot at rank 2. The gatekeeper mutation is underlined.
been confirmed to confer resistance to crizotinib (Ceccon et al., 2015). We generated similar results for the second-generation inhibitor ceritinib (Table 5), although, in this case, the G1269A mutation was not predicted to cause resistance, consistent with in vitro and clinical data (Gainor et al., 2016;Shaw et al., 2015). Entrectinib and lorlatinib are investigational ALK inhibitors currently in clinical trials. Increased half maximal inhibitory concentration values (decreased binding) in comparison with the WT protein have been demonstrated in in vitro experiments for several of the mutants prioritized by our workflow. In particular, G1269A  and G1202R  for entrectinib and G1269A (Gainor et al., 2016;Johnson et al., 2014;Shaw et al., 2015) and D1203N (Gainor et al., 2016) for lorlatinib. In addition, the G1123S and G1123D mutants were identified in an in vitro resistance screen for the ALK inhibitor TAE684 (Heuckmann et al., 2011) and in a patient resistant to ceritinib (Toyokawa et al., 2015). However, no data are currently available on entrectinib. The prioritized hotspot residues for ALK and corresponding reported clinical resistance mutations are provided in Table 5. A comprehensive list of all predicted resistance mutations is provided in Table S6.

DISCUSSION
We present a computational workflow to identify clinically relevant drug resistance mutations to targeted cancer therapies, both small molecule and biological. The workflow consists of consecutive filtering steps addressing three factors, which determine whether a mutation is likely to confer resistance in the clinic. Of these, the activity cutoff for protein-drug affinity compared with affinity for the endogenous ligand proved the most important and excluded 75.8% ± 1.1% (n = 18 [all 18 target-interaction partner case studies] ±SEM) of all potential mutants for each drug-target combination ( Figure 2C). This filtering step contains a fitness component; all mutants with predicted decreased affinity for the endogenous ligand (i.e., less fit clones), but with equivalent or increased affinity for the drug (i.e., more sensitive to drug treatment) are not progressed. In further considering the potential for disrupted binding of an endogenous ligand to mutated proteins, 8.6% ± 0.7% (n = 18, ±SEM) of mutants are predicted to completely abrogate binding of the endogenous ligand and were discarded. Removing triple-point mutations excluded a further 3.7% ± 0.2% (n = 18, ±SEM) of possible mutants. Interestingly, the majority of remaining mutations predicted to cause resistance (68.1% ± 1.7% [n = 18, ±SEM]) arise from DPM consistent with a similar proportion of DPMs observed in the Brenan dataset (Brenan et al., 2016). While DPMs commonly exhibit a low relP, they may become relevant when the predominant resistance clone harboring an SPM has been eradicated by cancer therapy. Taken together, 11.8% ± 1.0% (n = 18, ±SEM) of all evaluated mutations were considered to cause resistance ( Figure 2C).
The pool of potential resistant mutations was further analyzed to identify resistance hotspots where multiple different viable mutants are predicted to cause resistance to the drug under study. Mutants at these hotspots were prioritized based on their relP, which quantifies the relative probability of each specific amino acid mutation in the context of a defined cancer type. Applying this workflow, we correctly classify 84.5% of mutations  (Pao et al., 2005) L718 ( Table S4. a The number of mutants predicted to confer resistance (no. of pred mut) from the initial pool of possible mutants within 5 Å of the ligands (no. of all mut). b Resistance hotspots are identified and ranked according to the number of viable mutants (no. of mut) predicted for a residue. c The relP was calculated for all resistance hotspot mutations. Clinically observed resistance mutations and their rank according to relP (rank relP) are highlighted for each resistance hotspot. d NR, not reported--none of the predicted mutations are reported to confer resistance against the drug. The gatekeeper mutation is underlined.  (7) A269 (7) T315 (7) V256L Tiribelli et al., 2013) T315I (Gorre et al., 2001) NA e -Nilotinib 69 (540) V256 (8) A380 (8) NR NA -Y253 (6) A269 (6) T315 (6) G321 (6) T315I Redaelli et al., 2012;Weisberg et al., 2005) Dasatinib 58 (414) A380 (12) NR L248 (7) A269 (7) NR NA -Bosutinib 58 (414) V299 (7) T315 (7) G321 (7) V299L ( Table S5. a The number of mutants predicted to confer resistance (no. of pred mut) from the initial pool of possible mutants within 5 Å of the ligands (no. of all mut). b Resistance hotspots are identified and ranked according to the number of viable mutants (no. of mut) predicted for a residue. c Clinically observed resistance mutations are highlighted for each resistance hotspot. The relP could not be calculated as signatures for CML were not available. d NR, not reported--none of the predicted mutations were reported to confer resistance. In the case of axitinib, clinical resistance data on Abl are not yet available. e NA, not applicable, tied resistance hotspot at rank 1 or 2. The gatekeeper mutation is underlined.
in a comprehensive ERK2 mutation dataset and identify almost 40% of mutants conferring resistance to the ERK2 inhibitor SCH772984. Furthermore, we identify clinically observed drug resistance mutations within the top three predicted hotspot residues for first-generation compounds in all cases studied and for most second-and third-generation compounds where clinically observed resistance mutations have been reported. Importantly, in all cases studied, except ALK, the gatekeeper mutation is predicted among the top three resistance hotspots. In the ALK case study, two mutations that are also commonly observed in the clinic are highly ranked by our method. For the osimertinib-EGFR drug-target pair, we predicted resistance mutations before they were confirmed by clinical reports. Our workflow highlights mutations not yet observed in the clinic, or that may constitute FP predictions. The number of mutants progressed to further evaluation can be user defined. In the cases exemplified here, we prioritize three mutations with the highest relP for the top three resistance hotspots and significantly narrow the pool of potential resistance mutations for each drug from $350-1,200 possibilities to 9. While it may not be practical to further explore many hundreds of possible mutations, we suggest that a set of 9 prioritized mutants is more amenable to experimental testing within a drug discovery project or clinical setting. Thus, this workflow facilitates more focused monitoring of potential resistant mutations, as well as the design of next-generation compounds sensitive to likely resistant mutants.
Notably, our workflow evaluates the effect of a mutation on drug binding with respect to the WT protein. In some cases, exquisite drug affinity for the WT protein may mitigate a mutation-driven loss of potency. For example, our workflow correctly predicted the reduced affinity of lorlatinib for ALK G1269A; however, the exquisite potency of lorlatinib for WT ALK may render the relative loss of affinity for ALK mutants inconsequential (Gainor et al., 2016;Johnson et al., 2014;Shaw et al., 2015).
The workflow is dependent upon the availability and quality of both protein-ligand structural information and mutational signatures across diverse tumor types. For example, it is not clear which mutational signatures operate in CML, and, as a consequence, we could not determine the relP for mutants predicted to interfere with binding of Abl inhibitors in CML.
The approach presented here investigates the effects of protein mutation on drug binding mode as characterized by the input protein-ligand structure. How such mutations affect the overall conformation of the target protein and/or drug-target complex is not encompassed by our method. Extension of the approach by considering the effect of residue mutation on global protein conformation and by inclusion of structural models for highly homologous protein families would further expand the potential impact and is the aim of our future studies. For example, FP mutations that stabilize inactive protein conformations that are unlikely to persist could be excluded, and mutations that further stabilize an active protein conformation could be included. Furthermore, drug-resistant mutations distant from the binding site, including those which influence protein flexibility and conformation, which are beyond the scope of our current method, could also be evaluated.
This workflow includes three critical determinants of clinically relevant drug-resistant mutations. Importantly, the workflow can be further adapted to test more specific hypotheses. For example, Osprey parameters can be changed to include larger backbone movements and/or to include multiple ligand rotamers; furthermore, the number of prioritized mutants can also be user-defined.
In conclusion, we have developed and validated a computational method to prospectively identify clinically relevant drugresistance mutations. We suggest that this approach can have a significant impact on the design and development of targeted therapies by proactively signposting drug resistance hotspots. Prior knowledge of resistant mutants enables their timely detection in patients and the early development of effective treatment options against the resistant tumor cell population.

SIGNIFICANCE
Although molecularly targeted cancer therapies have shown great success in the clinic, drug resistance has emerged as the major challenge. Resistance mutations are commonly identified and characterized during clinical evaluation, often resulting in a reactive approach to tackling drug resistance. We report a computational method to prospectively identify drug resistance mutations during the design phase of potential therapeutics. This approach enables early signposting of likely resistance hotspots and supports more focused monitoring of potential emergent resistant clones as well as the timely development of alternative treatment options.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:

AUTHOR CONTRIBUTIONS
T.K. conducted the experiments. J.B. and T.K. designed the experiments and wrote the paper.

DECLARATION OF INTERESTS
The authors declare no conflict of interest.

STAR+METHODS KEY RESOURCES TABLE CONTACT FOR REAGENT AND RESOURCE SHARING
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact Julian Blagg (Julian.blagg@icr.ac.uk).

DATA AND SOFTWARE AVAILABILITY
Data are available upon request to the Lead Contact.

Structure Selection and Preparation
When available, crystal structures of wild type protein were used. Formation of the Lys-Glu salt bridge was the minimum requirement for kinase co-factor structures to be considered representative of an active conformation. Due to the paucity of high resolution ATP-bound kinase protein structures, we employed ADP or ATP analogue complexes for all kinase structures representative of endogenous co-factor binding. A list of employed PDB entries is provided in Table S7. All crystal structures were prepared using the Preparation wizard (Sastry et al., 2013) in Maestro version 9.8.016 (Schrö dinger Release, 2014). Bond orders were assigned and hydrogens were added. Zero-order bonds to metals and disulphide bonds were created and selenomethionines were converted to methionines. Missing side chains were added and all waters beyond 5 Å from heteroatoms, buffer compounds, and additional chains were deleted. Ionization states were generated using Epik. H-bonds were assigned, water conformations were sampled and structures minimized using default settings.
Residues within 12 Å of the ligand were selected using the selector tool in MOE 2015.1001 (Molecular Operating Environment (MOE), 2017) and only selected residues were included in the input structure. As described in the Osprey documentation, the protonation state of histidines was defined in the pdb input file, all HETATM identifiers were replaced by ATOM, and chain information was deleted.

Evaluation of Ligand Affinity
Osprey version 2.2beta Gainza et al., 2013), using the Amber94 force field, was employed to evaluate the impact of protein mutation on ligand binding. All non-peptide interaction partners were parameterized using Antechamber (AmberTools16 ) as described in the Osprey documentation and only the selected crystal structure protein conformation was used. Small molecule ligands were allowed to rotate and translate during the calculations.
Residues within 5 Å of the ligand were systematically mutated to all other possible amino acids, except if they were known to be crucial for catalytic activity; namely the AspPheGly motif, catalytic Asp, and Lys-Glu salt bridge in kinases. All three His protonation REAGENT


extfieldsofstudy: Medicine Chemistry

s2fieldsofstudy: Biology Medicine

text:
Age-related collagen turnover of the interstitial matrix and basement membrane: Implications of age- and sex-dependent remodeling of the extracellular matrix

The extracellular matrix (ECM) plays a vital role in maintaining normal tissue function. Collagens are major components of the ECM and there is a tight equilibrium between degradation and formation of these proteins ensuring tissue health and homeostasis. As a consequence of tissue turnover, small collagen fragments are released into the circulation, which act as important biomarkers in the study of certain tissue-related remodeling factors in health and disease. The aim of this study was to establish an age-related collagen turnover profile of the main collagens of the interstitial matrix (type I and III collagen) and basement membrane (type IV collagen) in healthy men and women. By using well-characterized competitive ELISA-assays, we assessed specific fragments of degraded (C1M, C3M, C4M) and formed (PINP, Pro-C3, P4NP7S) type I, III and IV collagen in serum from 617 healthy men and women ranging in ages from 22 to 86. Subjects were divided into 5-year age groups according to their sex and age. Groups were compared using Kruskal-Wallis adjusted for Dunn’s multiple comparisons test and Mann-Whitney t-test. Age-specific changes in collagen turnover was most profound for type I collagen. PINP levels decreased in men with advancing age, whereas in women, the level decreased in early adulthood followed by an increase around the age of menopause (age 40–60). Sex-specific changes in type I, III and IV collagen turnover was present at the age around menopause (age 40–60) with women having an increased turnover. In summary, collagen turnover is affected by age and sex with the interstitial matrix and the basement membrane being differently regulated. The observed changes needs to be accounted for when measuring ECM related biomarkers in clinical studies.

Introduction
The extracellular matrix (ECM) is the backbone of all tissues. It is composed of several structural proteins, including collagens, which play a vital role for the function and maintenance of normal tissue function. Collagen type I and III are the most abundant collagens of the interstitial matrix and essential for its structure. The basement membrane, underlying epithelial or endothelial cells, primarily consist of collagen type IV which ensure optimal cell polarization and function [1,2].
ECM tissue turnover, i.e. the tight balance between protein degradation and formation can be classified as two processes: 1) tissue modeling, occurring during development and growth where new tissue is being generated; 2) tissue remodeling, where functional tissue is being maintained by replacing old and damaged proteins with new ones [3,4]. Every healthy organ is undergoing continuous remodeling with a tight control between degradation and formation. However, this delicate balance might be disturbed, leading to connective tissue disorders such as fibrosis and cancer [5]. Measurements of ECM turnover products in blood have shown that circulating components of the ECM, especially collagens, are elevated in fibrotic diseases [6][7][8][9][10] and cancer [11,12]. We have developed a panel of serum-based assays specifically measuring collagen fragments that reflect either degradation or formation separately [13][14][15][16][17][18]. The principle behind these assays is the use of monoclonal antibodies exclusively reacting with a specific fragment of a certain protein which become exposed after specific protease-mediated degradation. Antibodies raised against pro-peptides of pro-collagens reflect collagen formation whereas antibodies recognizing small neo-epitopes on peptides derived from collagen degradation of the triple helical region represent collagen degradation [5,19,20]. Table 1 summarizes the assays we have used in this study and which ECM process they represent.
PINP, which measures the N-terminal pro-peptide released during collagen formation, have been showed to primarily reflect synthesis of bone matrix [21]. C1M, which measures a MMP-degraded fragment of type I collagen released during tissue remodeling, is closely related to chronic inflammation with high levels being present in various inflammatory diseases [14,[22][23][24][25]. Pro-C3 measures the pro-peptide of type III collagen, i.e. synthesis, and C3M measures a MMP-generated type III collagen fragment, i.e. degradation. Increased levels of Pro-C3 and C3M have also been linked to inflammatory diseases and especially fibrosis [25][26][27][28][29][30]. P4NP7S, which reflects type IV collagen formation by measuring the 7S domain of type IV collagen, has been associated with fibrosis of the liver [31] and C4M, which reflects MMPmediated degradation of the basement membrane, is elevated in patients with diseases displaying chronic inflammation [18,24,[32][33][34].
While it has been shown that levels of ECM proteins vary significantly with age [35][36][37][38][39], a collagen turnover profile in men and women of varying age using a well-defined panel of markers directly measuring degradation and formation of interstitial matrix and basement membrane collagens has never been publihsed. In this study, we investigated age-and sex- dependent ECM turnover as function of age in healthy men and women by measuring biomarkers of formation and degradation of the most abundant collagens of the interstitial matrix (collagen type I and III) and basement membrane (collagen type IV) in serum.

Serum samples
The serum samples used in this study originated from a population-based cross-sectional study where subjects were recruited in 2007-2011 from a random sample of all districts in Berlin provided by the resident registration office. A total number of 617 subjects, comprising 303 healthy men and 314 healthy women aged 22-86, were included in this study. Characteristics of the study population are presented in Table 2.
Venous blood samples were collected by trained medical technologists between 08:00 and 10:00 AM after a 12 h fasting period. 30 min. after blood drawing, samples were centrifuged at 3500 rpm for 10 min, serum was obtained and samples were stored at -80˚C until analysis.
The study was approved by the local ethics committee (Ethikkommission der Charité, Charité -Universitätsmedizin Berlin, EA4/095/05) as well as the German Radiation Protection Ordinance (Z5-22462/2-2005-063). Written informed consent was obtained from all participants before they were included into the study and the study was carried out in accordance with ICH-GCP and according to the Declaration of Helsinki.

Collagen turnover biomarker analysis
All biomarker assays were manufactured by Nordic Bioscience (Herlev Denmark) and performed according to the manufacturer's specifications. The assays are competitive ELISA's which have been thoroughly validated for their use in human serum samples and technically characterized with regards to linearity, accuracy and reproducibility (the original assay reference for each biomarker is listed in Table 1). To eliminate/reduce inter-assay variation, all biochemical markers were measured using a single lot of reagents and serum controls were included on each plate. Females and males, as well as age-groups were randomly distributed on the different ELISA plates. All samples were analyzed by a CAP/CLIA-accredited laboratory (Nordic Bioscience Laboratory, Herlev, Denmark).
The neo-epitope biomarkers of matrix metalloproteinase (MMP) degraded type I, type III and type IV collagen (C1M, C3M, C4M) and type I, type III and type IV collagen formation products (PINP, Pro-C3, P4NP7S) were assessed in serum as previously described [13][14][15][16][17][18]. Briefly, 96-well pre-coated streptavidin plates were coated with biotinylated synthetic peptides specific for the protein of interest and incubated for 30 minutes at 20˚C. 20 μL of standard peptide or pre-diluted serum sample were added to designated wells followed by the addition of peroxidase-conjugated specific monoclonal antibodies and incubated for 1 h at 20˚C or overnight at 4˚C. Finally, tetramethylbenzinidine (TMB) (cat. 438OH, Kem-En-Tec Diagnostics, Denmark) was added to each well and the plates were incubated for 15 minutes at 20˚C. All incubation steps included shaking at 300 rpm and after each incubation step, the plates were washed five times with wash buffer (20 mM Tris, 50 mM NaCl, pH 7.2). The enzymatic reaction was stopped by adding 0.18 M H 2 SO 4 and absorbance was measured at 450 nm with 650 nm as reference. A calibration curve was plotted using a 4-parameter logistic curve fit.

Statistical analysis
Subjects were stratified into 5-year interval age groups. To investigate if the biomarkers changed with age, the levels of the individual biomarkers in each 5-year age group were  compared using a Kruskal-Wallis ANOVA (S1 Table). The p-values were adjusted to account for multiple comparisons using Dunnett´s method. An increase or decrease is defined as a significant difference between two following age groups, e.g. age group 25-29 vs. 30-34 (two adjacent age groups).
To investigate if the biomarkers varied according to sex, the level of the individual biomarkers between men and women in the five-year age groups was compared using the Mann-Whitney test. Serum biomarker data can be found in S1 Data. Graph design and statistical analyses were performed using GraphPad Prism version 7.03 (GraphPad Software, Inc., CA, USA).

Age specific changes in collagen remodeling
Type I collagen. Type I collagen formation and degradation were measured with the PINP and C1M assays, respectively. In men, the highest level of PINP was found at age 20-24 ( Fig 1A).
The level declined with increasing age. Comparing the level at age 20-24 with the rest of the age-groups, a significant decrease was observed from age 45-49 to 80+ (S1 Table). In women, the highest level of PINP was found at age 20-24 and 80+. PINP levels decreased from age 20-24 until age 35-39, but not significantly (Fig 1A). Around menopause (average age 40-60 [40]) the level significantly increased until age 60-64 (S1 Table).
The level of C1M in men was relatively stable with no significant changes ( Fig 1B). The level of C1M in women was also relatively stable with only a significant increase between age group 40-44 and 70-74 ( Fig 1B) (S1 Table).  and (B) degradation of interstitial type I collagen (C1M). Statistical significance of C1M and PINP between each age group was calculated using ANOVA comparing the mean of each group with the mean of every other group and is presented at the different age groups in S1 Table. All data are shown as median and interquartile range. Type III collagen. The ELISA assays Pro-C3 and C3M were used to measure type III collagen formation and degradation, respectively. Pro-C3 levels in men showed a significant decrease between age 20-24 and 45-49 followed by a significant increase until age 80+ (Fig  2A) (S1 Table).
The level of Pro-C3 was relatively stable in women with no significant changes until age 45-49. A significant increase from age 45-49 and 55-59 until age 80+ was observed (Fig 2A) (S1 Table).
The level of C3M in men was relatively stable from age 20-80+ with no significant changes ( Fig 2B). The same pattern was seen in women ( Fig 2B). However, around menopause (average age 40-60 [40]) the level significantly increased until age 55-59 (S1 Table) followed by a steep significant decline (S1 Table).
Type IV collagen. Type IV collagen formation and degradation were measured with the P4NP7S and C4M assays, respectively. The level of P4NP7S in men and women was relatively stable with no significant changes across the age span ( Fig 3A).
The same pattern was observed with C4M in men ( Fig 3B). In women, a significant increase was observed from age 25-29 reaching a maximum at age 55-59 ( Fig 1F) (S1 Table). The level significantly decreased reaching its starting level at age 80+ (S1 Table).

Sex specific changes in collagen remodeling
Type I collagen. For PINP, the starting level was significantly higher in men compared to women until the age of 45-49 (Table 3). Around this age, the level starts to increase in women, whereas the level in men continues to decrease. In the postmenopausal period (age 60-80+), the level was significantly higher in women compared to men, except at the age of 70-79 (Table 3).
For C1M, no significant difference between the sexes was observed. Type III collagen. The level of Pro-C3 was significantly different in women compared to men in specific age groups in the end of the menopausal period (age 55-59) and the postmenopausal period (70-74 and 75-79) with men having the highest level (Table 3).
For C3M, a significantly higher level was observed in women as compared to men at the age of 55-59 (menopausal period) ( Table 3). Type III collagen turnover as function of age. Biomarkers reflecting degradation and formation of type III collagen were measured in serum from 303 healthy men and 314 healthy women aged 22-86 divided into 5-year age groups. (A) Formation of interstitial type III collagen (Pro-C3) and (B) degradation of interstitial type III collagen (C3M). Statistical significance of C3M and Pro-C3 between each age group was calculated using ANOVA comparing the mean of each group with the mean of every other group and is presented at the different age groups in S1 Table. All data are shown as median and interquartile range. Type IV collagen. As with C3M, a significantly higher level of P4NP7S in women at age 55-59 was observed ( Table 3). The level of C4M was significant different in men and women at age 20-24 and 55-59 with women having the highest level (Table 3).

Discussion
The present study defines a collagen turnover profile of the main collagens of the interstitial matrix (type I and III collagen) and basement membrane (type IV collagen) as function of age in healthy men and women. The main findings of the study were: 1) age specific changes in collagen turnover was most profound for type I collagen with its formation being strongly agedependent and 2) sex specific changes in collagen turnover was most apparent during the menopausal and post-menopausal periods with the interstitial matrix and basement membrane being differently regulated. To our knowledge, this is the first time a collagen turnover profile measuring collagen formation and degradation separately, has been established in healthy men and women across the age span of adults.
Type I collagen is the main ECM component of bone, composing 90% of the organic matrix [41] and its formation seems to be strongly age-dependent ( Fig 1A). The level of PINP was higher in young men and women compared to older subjects with a progressive decrease until the age around 50. This observation is in accordance with previous studies showing a decrease in women between age 30-35, stable level from age 35-45 followed by an increase until the age of 65 [35,37]. In men, a significant decrease with age has also been observed [37]. The decline in PINP levels until middle-age may reflect a shift from bone modeling (formation) in childhood and early adulthood to bone remodeling (maintenance). As seen in Fig 1B, degradation of type I collagen (C1M) is relatively stable throughout the study period. As C1M is a marker of tissue inflammation and does not represent bone degradation [14], this may explain why C1M is not age-dependent and PINP is. One may argue that elder subjects have more inflammation than younger, hence a rise in C1M late in life was expected. However, the observed results may reflect that the elder subjects in this study are "super" healthy compared to the "normal" elder population.
Type III collagen is one of the main interstitial collagens and structurally similar to type I collagen. Despite being often associated with type I collagen, the formation of type III collagen showed a different pattern with an increase in both men and women after age 45-49 and . Statistical significance of C4M and P4NP7S between each age group was calculated using ANOVA comparing the mean of each group with the mean of every other group and is presented at the different age groups in S1 Table. All data are shown as median and interquartile range. https://doi.org/10.1371/journal.pone.0194458.g003 Age-and sex-dependent collagen turnover of the extracellular matrix The level of the individual biomarkers between men and women in the five-year age groups was compared using the Mann-Whitney test. P-values are indicated and represent significant difference (p 0.05) between men and women. https://doi.org/10.1371/journal.pone.0194458.t003 Age-and sex-dependent collagen turnover of the extracellular matrix 55-59 respectively. As Pro-C3 is a marker of fibrosis [15,29,42], these results suggest that tissue fibrosis increases with advancing age. Type IV collagen is one of the most abundant components of the basement membrane. No association between age and biomarker level (P4NP7S and C4M) was observed, however C4M in women was significantly increased with time comparing age 25-29 with age 55-59 (Fig 3B,  S1 Table). These data suggest that basement membrane degradation is increased in the menopausal period.
Karsdal et al. [3] have conducted a study similar to this in rats. They found that type I collagen had an increased turnover in younger rats compared to old rats consistent with our findings. Type III collagen turnover was not significantly influenced by age while type IV collagen degradation was slightly upregulated in younger animals. These results can be translated into the present data in humans which is of importance when conducting pre-clinical and clinical studies in ECM remodeling diseases.
To investigate sex-specific changes in collagen turnover, the level of the individual biomarkers between men and women in the five-year age groups was compared. C3M, P4NP7S and C4M showed a significant difference between men and women in the menopausal period (age 40-60) with women having a higher level of collagen remodeling. PINP and Pro-C3 displayed an increase in women compared to men in the post-menopausal period (after the age of 60). PINP showed a significant lower level in women throughout the pre-menopausal period (before the age of 40). Together, these data suggest that hormonal status might affect collagen remodeling. Whether this is related to estrogen levels or other menopausal mechanisms needs further investigations. However, the fact that the biomarkers decreases again during the post-menopausal period indicates that a specific event during menopause is somehow influencing the collagen turnover. These sex-specific changes in collagen turnover could be associated with diseases that is more prevalent in postmenopausal women, as with osteoporosis and PINP [43].
One limitation of this study is the lack of younger subjects. Studies measuring the aminoterminal pro-peptide of procollagen type III have shown a high level in young children which decreased with advancing age with a short increase around puberty [38,39]. These data may reflect the process of modeling where new tissue is being formed during growth whereas the results of the current study only reflects the end of this process involving a shift towards ECM remodeling. Another limitation is the lack of information on the subject's renal function. We are currently investigating the association between renal function and the collagen turnover biomarkers. Unpublished data from our group show no association between renal function and C1M, PINP, C3M, PRO-C3 and C4M.
Subjects with ECM remodeling disorders, such as fibrosis and cancer, have been shown to have a different ECM turnover compared to healthy individuals [18,22,25,29,33,44] and attention has been drawn towards the involvement of matrix proteins in ECM remodeling disorders [45,46]. For example in cancer, it is now becoming evident that the tumor milieu, i.e. the ECM, is just as important as the tumor itself for tumor progression and metastasis [47,48]. The ability to measure changes in ECM turnover in ECM-involved pathologies, could be an important step towards a better personalized medicine [49,50].
The presented data are important to consider when conducting clinical studies focusing on ECM-related disorders as these biomarkers have been shown to associate with various connective tissue disorders where the ECM balance is skewed [5,15,18,22,25,29,33,44].

Conclusions
In conclusion, collagen turnover is affected by age and sex with the interstitial matrix (type I and III collagen) and the basement membrane (type IV collagen) being differently regulated.
We have established an age-and sex-dependent collagen turnover profile in healthy men and women and the observed changes needs to be accounted for when measuring ECM related biomarkers.
Supporting information S1 Table. Age-related changes in collagen remodeling. Statistical significance (p-value) for each biomarker calculated using ANOVA comparing the mean of each group with the mean of every other group. Ns refers to no significance. The following biomarkers showed no significance and are therefore not presented: C1M (men), C3M (men), P4NP7S (men and women) and C4M (men). (DOCX) S1 Data. Serum biomarker data for each subject. (XLSX)


extfieldsofstudy: Medicine Biology

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Cancer Stem Cells or Tumor Survival Cells?

Abstract Research endeavors originally generated stem cell definitions for the purpose of describing normally sustainable developmental and tissue turnover processes in various species, including humans. The notion of investigating cells that possess a vague capacity of “stamm (phylum)” can be traced back to the late 19th century, mainly concentrating on cells that could produce the germline or the entire blood system. Lately, such undertakings have been recapitulated for oncogenesis, tumor growth, and cancer cell resistance to oncolytic therapies. However, due to the complexity and basic life-origin mechanisms comprising the genetic and epigenetic repertoire of the stemness in every developing or growing cell, presently there are ongoing debates regarding the biological essentials of the stem cell-like tumor initiation cells (ie, cancer stem cells; CSCs). This conceptual analysis focuses on the potential pitfalls of extrapolating that CSCs bear major traits of stemness. We propose a novel nomenclature of Tumor Survival Cells (TSCs) to further define tumor cells behaving like CSCs, based on the ruthless and detrimental features of Cancer Cell Survivology that appears fundamentally different from stem cell biology. Hence, precise academic separation of TSCs from all the stem cell-related labels applied to these unique tumor cells may help to improve scientific reasoning and strategies to decode the desperado-like survival behaviors of TSCs to eventually overcome cancer.

Background
E rnst Haeckel (1834-1919), a German biologist, physician, naturalist, artist, and philosopher, is considered a pioneer in developmental cell biology research. He proposed the nomenclature of ''Stammzellen'' (stem cells) in his published lectures on ''Natürliche Schöpfungsgeschichte'' (1868) for unicellular organisms or protozoa that he thought to be the phylogenetic ancestors of multicellular organisms [1]. He considered that the name stem cell seemed to be the most explicit and appropriate one for a pluripotent cell phenotype, from which all other cells stem. They are, in the most literal sense, the stem father and the stem mother of all the infinitive generations of cells that the multicellular organism is ultimately built with [1]. The term was created to distinguish the unique profile of the fertilized egg cell from the original egg cell. Following the doctrine, the human stem cell directly represented the whole future child [2,3].
Haeckel's neologism was derived from the metaphorical language popularly used back then in medical and philosophical discussions about cells and the human body.
Scholars (eg, Rudolf Virchow: 1821-1902) often compared cells in a given organism with human individuals within an established state system [4]. For the first time, the concept of a stem cell defined cell state in a hierarchical and centralized format, departing from the previous conception of a liberal and relatively egalitarian profile. It is worth noticing that (1) stem cells are primordial biological entities destined to build a homeostatic system that can reproduce itself, and (2) the metaphorical implication of the stem cell concept not only has its general public education value, but more importantly, its usage deeply impacts the way scientists frame and orient their reasoning.
For example, at the turn of the 19th century, Artur Pappenheim , Alexander Maximow (1874-1928), Ernst Neumann (1798-1895), and other scholars proposed a progenitor cell-based theory for the common origin of all hematopoietic lineages. In the beginning of the 20th century, the advancements in the field of hematopoiesis and leukemia research further distinguished the stem cell definition, underscoring a common central capacity for self-renewal and phenotypic differentiation [2].

The Evolving Theory of Cancer Stem Cells
Possible underlying relations between embryonic stem cells (ESCs) and normal tissue or cancer-like neoplasm were also speculated in the late 19th century. The notion concerned the chances for development deviations of ESCs to contribute to malformation or tumorigenesis [5]. However, key components of this tumorigenic theory (eg, the displacement of embryonic cells) were questioned by gathering experimental evidence around World War II [6]. In the 1950s and early 1960s, systematical investigation of murine teratoma cells resulted in successful isolation of mouse ESCs and basic characterization techniques. The research progression further cultivated the postulation of existence of the so called cancer stem cells (CSCs) [7]. By the early 1980s, murine ESCs could be reliably isolated and maintained in vitro [8,9], which, together with the identification of human neural stem cell and human ESC lines laid down the foundation for opening the contemporary chapter of stem cell research [10][11][12].
In parallel, the concept of CSCs was gradually shaped out in the 1960s. For instance, Kleinsmith and Pierce demonstrated that donor embryonal carcinoma cells (ECCs) could give rise to both somatic tissue cells and ECCs [7]. It was reported that only *0.1%-1% of murine myeloma cells could give rise to new clones in vitro, and only *1%-4% of leukemia cells formed macroscopic colonies in the spleen after transplantation in nonobese diabetic/SCID (severe combined immune deficiency) mice [13]. Noticeably, the data showed certain similarities with the formation of nodules that was observed in the spleens of irradiated mice following administration of bone marrow cells. The number of nodules generated was found to be dose dependent on the quantity of the injected bone marrow cells. Thus, the investigators hypothesized that a single hematopoietic stem cell (ie, colony-forming unit) might be able to develop into a cell colony that gradually formed an individual nodule [14]. These findings combinatorially inferred the possibility that a limited number of tumor cells might have ''stem celllike'' oncological behavior and act as a ringleader for tumor initiation. Taken together, these discoveries promoted the establishment of the CSC theory.
By the mid-1970s, the clonal evolution theory of cancer growth was additionally enriched by uncovering that mutations in oncogenes and tumor suppressor genes played important roles in tumorigenesis [15]. Fearon and Vogelstein proposed that the stepwise acquisition of mutations in specific oncogenes was critical in the progression and malignization of early adenoma, based on their clonal evolution model of colon cancer [16]. The feature of colon cancers indeed exhibited a generally linear tumor evolution with incremental genetic mutations following inactivation of adenomatous polyposis coli as the most common gene mutation. Elucidating these genetic mechanisms helped to address the question of why a given malignant tumor lesion may contain a subpopulation of cells that show everescalating malignant behavior [16]. By contrast, breast cancers retain discernible levels of intratumoral heterogeneity [17]: for example, amplification of HER2 (human epidermal growth factor receptor 2), mutation of PIK3CA (phosphoinositide-3-kinase, catalytic, alpha polypeptide), etc. Moreover, similar heterogeneity exists in leukemia. Nearly all subtypes of acute myeloid leukemia (AML) can be implanted in immunodeficient mice by engraftment of a CD34 + CD38fraction of AML cells (ie, acute myelogenous leukemia stem cells, LSCs: *1/million AML cells) [18].
At the beginning of the 21st century, the concept of CSC or tumor stem cell was refined based on the evidence that certain developmental signaling pathways governing regular stem cells might also function in CSCs for tumor formation [19]. Therefore, CSCs, as a small subpopulation of tumor cells, were proposed as the primary force fueling oncogenesis and were characterized by their capability of infinite self-renewal and drug resistance [19]. For example, to initiate a new tumor in a mouse, only *100 CD44 + CD24 -/low human breast cancer cells are required, indicating their potent cancerogenic potential relative to other phenotypes of tumor cells that fail to grow tumors even under thousandfold higher quantity [20] . In support to our hypothesis, TSCs possess genes either uniquely to themselves (markers in red zone and red font) or shared with ESCs (molecules in yellow zone) that are predominantly related to cell survival functions (eg, proliferation, migration, invasive growth, drug resistance, etc.; markers in red fonts). They play critical roles in cancer metastasis, reoccurrence, and oncolytic treatment failure. Although TSCs in different types of malignant tumors share numerous molecular markers with ESCs (markers in yellow zone), they are deficient in molecules that are essential for the maintenance of pluripotent status, selfrenew, and lineage-specific differentiation, key features of authentic stemness for physiological development and reproduction of biological organisms/entities, including humans (markers in green fonts). This unbalanced desperado-like survival strategy of TSCs disrupts homeostasis and exhausts resources essential for host life, which inevitably leads to demises of both host and tumor cells (left panel flowcharts). By contrast, totipotent stem cells, inner cell mass-derived ESCs, carry stemness genes mostly for physiological development (markers in green font and green zone) and keep an effective balance between cell development (markers in green font), tissue formation (eg, markers in yellow: for cell differentiation), and survival (markers in red font; note: functions of the marker molecule in black font are presently unclear). These genes work in consortium to drive proper cell proliferation and migration, lineage differentiation, organ genesis and systemic homeostasis, and to make the biological species sustainable. For example, human ESCs differentiate into progenitor cells of the three primary germ layers that subsequently establish functional tissues, organs, and systems. With a new embryo implantation and growth, the whole process of ESC-originated development results in a sustainable life circle for human race (right panel flowcharts). The process defines the authentic stemness capacity (ie, stamm or phylum). CSC, cancer stem cell; Drug Resi., drug resistance; ESC, embryonic stem cell; Gpj, gap junction; HSC, hematopoietic stem cell; MDR, multiple drug resistance; NSC, neural stem cell; PT, posttreatment; TSC, tumor survival cell. Color images available online at www.liebertpub.com/scd TUMOR SURVIVAL CELLS 1467 Cells. The definition states that CSCs should possess the properties of tumorigenicity, self-renewal capacity, continuous passage ability, multilineage differentiation potential to generate the heterogeneous subpopulations of cancer cells that comprise malignant tumors, and unique and reliable surface markers [20][21][22][23].

Reciprocal Interaction Between CSC and Normal Stem Cell Research Studies
The CSC theory, besides its academic impact, has practically revealed new therapeutic targets for designing specific therapies to treat cancer. To this end, knowledge gleaned from investigating normal stem cells has markedly improved the understanding of the heterogeneous nature of cancer cells [19,23]. It is believed that CSCs hold higher oncologic plasticity than regular cancer cells; this plasticity may be powered mainly by stemness-like capabilities, promoting efforts to identify key triggers of neoplasm occurrence/recurrence, metastasis, and drug resistance [19][20][21][22][23][24][25]. Therefore, it is pivotal to investigate whether or to which scale CSCs may genetically overlap with normal stem cells. Ultimate understanding of these mechanisms will facilitate therapeutic development for managing cancer.
Indeed, a subgroup of CSCs has been found to behave as tumor metastasis and recurrence (or drug resistance)-initiating cells due to their quiescent state, tumorigenicity, and migration capabilities. The cells express markers of epithelial mesenchymal transition (EMT), including collagen IV a1, a-SMA, b-catenin, etc. [24]. EMT describes the process of the transdifferentiation of stationary epithelial cells into motile mesenchymal cells. Over EMT evolvement, epithelial cells lose their tight junctions and apical-basal polarity, reorganize their cytoskeleton, and experience changes in the signaling cascades that control cellular morphological features and gene expression programs. These alterations share main features of dedifferentiation, increasing the motility of individual cells, and enabling them to develop into phenotypes with invasive behaviors that are crucial for cell survival. EMT process can be regulated or influenced by multiple pathways that are activated by TGF-b, HGF, EGF, FGF, VEGF, Wnt, Shh, IL6, HIF1a, and other signaling molecules through SNAI1/Snail, ZEB1/ZEB2, and/or basic helix-loophelix proteins-mediated transcription activity [25]. EMT events are crucial in major biological courses of embryonic development, postnatal growth, tissue regeneration, lesion healing, and stem cell homeostasis. EMT-like mechanisms have also been implicated in triggering oncology of malignancies and pathophysiology of fibrosis [25]. The involvement of dedifferentiation as a stem cell-like feature (ie, stemness) in some cancer cells to drive tumorigenesis has been further validated by new findings published in The Pan-Cancer Atlas, the official data portal of The Cancer Genome Atlas (TCGA) consortium. Specifically, TCGA tumors' (ie, 11,000 tumors from 33 of the most prevalent forms of cancer) epigenetic and expressionbased stemness indices measured oncogenic dedifferentiation and revealed association with oncogenic dedifferentiation. The investigators reported that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. The indices identified novel targets for designing potential therapies to augment tumor differentiation [26]. Importantly, it has been recognized that dedifferentiation is likely a mecha-nism for cell survival [27]. We, therefore, suggest to also focus on survival benefits that can be derived from the intratumor molecular heterogeneity determined by the stemness indices reported [26], to dissect it from classically defined stemness indices of developmental biology that emphasize differentiation [28].

CSCs Do Not Possess Authentic Stemness Biology
Researchers have determined a variety of surface markers for identifying CSCs. As examples, currently well-accepted markers for glioma CSCs include CD15, CD90, CD133, nestin, and integrin-a6. CD44, ALDH, CD117, CD133, and CD24 are utilized to profile ovarian CSCs. For malignant melanoma CSCs, ABCB5, ALDH1, CD20, CD133, and CD271 are commonly enlisted. ALDH1, CD44, CD24, CD90, and CD133 are highlighted as markers for breast CSCs. There are some shared CSC markers frequently expressed in different types of malignant tumors. Among them, CD133 appears to be the most common one, which coincidently is also a marker of normal stem cells (eg, undifferentiated ESCs, HSCs, and NSCs).
However, in spite of accumulation of published data that is in favor of the CSC concept, the validity of the stemness biology in CSCs has been constantly challenged by observations concerning discrepancies regarding the biological characteristic, phenotype, genetic profile, and subpopulation proportion ratio of the alleged ''original'' CSC. Studies showed that successful isolation rate of glioma CSCs was dependent on microenvironmental specifics, including cellcell interaction, culture medium composition, and cell incubation temperature [29,30]. The data begin to contest the existing CSC theory and arguably suggest that these cells could be a reactive dedifferentiation consequence of regular cancer cells driven by microenvironmental stress, attempting to maximize the survival probability of the tumor, rather than an outcome of a conventionally defined hierarchical cascade of tumor cell development. In fact, CSC-produced intratumor heterogeneity is incapable to form any truly sustainable biological system such as normal tissues or organs. The dedifferentiated tumor cells seemed to be destined to refill the pool of previous CSCs upon their depletion resulting from regular cancer cell differentiation, host immune counteraction, and/or anticancer treatment [31]. Thereby, we hypothesized that the commonly defined CSCs might mostly retain genes underlying cell survival (eg, dedifferentiation, proliferation/self-renewal, migration, engraftment, and drug resistance) relative to those of normal stem cells that fundamentally concern lineage-oriented differentiation, homeostasis, stemness, and sustainability through self-renewal and reproduction (Figs. 1 and 2 and Table 1). To test this hypothesis, we systematically examined a total of 50 established molecular markers of CSCs and/or ESCs. The results demonstrated that (1) CSC exclusive markers are genes that support cancer cell migration, metastasis and invasion, and/or enable drug resistance capability (see Figs. 1 and 2 for red markers) [32][33][34][35][36][37][38][39][40][41][42], and only two of them bear uncertain functions (gray markers: CD96 [43] and PSCA [44]; Fig. 2); (2) the majority of markers shared by CSCs and ESCs are genes that are also related to cell migration and metastasis or engraftment (see Figs. 1 and 2 for red markers [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59]61]) [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], with one related to proliferation but not stemness (brown marker: stage-specific embryonic antigen-3; SSEA3; Fig. 2) [64], and a few concerning self-renewal and maintenance of pluripotent status or dedifferentiation (green markers: HMGA2 [65], GJA [66], Oct-3/-4 [67], and Sox [68]; Therefore, the analytical outcome in general confirms our postulation. Although CSCs share many genetic markers with ESCs, deeper dissection revealed that CSC-specific genes are predominantly in charge of cell survival activities typically involving invasive growth, cell migration, and survival adaptation (eg, intratumoral heterogeneity and drug resistance), which jointly play critical roles in cancer metastasis, reoccurrence, and insensitivity to chemotherapy and host immune counteractions. By contrast, inner cell mass-derived ESCs exhibit a balanced profile between genes responsible for authentic stemness maintenance emphasizing pluripotency, self-renewal, capability of lineage-specific differentiation and development into reproducible organisms, and genes empowering cell homeostatic survival ( Fig. 1; see detailed information in Table 1). Evidently, the biological trajectories of CSCs, which are hallmarked by unilateral attempts for self-survival at the expense of regular cancer and host cells, do not match natural paradigms of developmental biology-related stem cells. Normal stem cells, alongside their proliferation, migration, and differentiation, constantly build homeostasis with surrounding cells through their functional multipotency [82]; and once differentiated into terminal phenotypes, they will not dedifferentiate under physiological conditions.
In corroboration with our analysis, published experimental results clearly demonstrate that all colonies derived from randomly selected single cells of murine lung and breast cancer cell lines can form tumors following allografting in histocompatible mice [83]. A recent study reported that using an approach that integrated major immunogenomics methods (ie, total lymphocytic infiltrate assessed from genomic and haemotoxylin and eosin staining image data, immune cell fractions from deconvolution analysis of mRNA-sequencing data, immune gene expression signatures, neoantigen prediction, T cell receptor and B cell receptor repertoire inference, viral RNA expression, and somatic DNA alterations) to characterize the immune tumor microenvironment (TME) (ie, the immune subtype), investigators identified six immune subtypes that span TCGA cancer tissue types and molecular subtypes [84]. Cancer immune subtypes differ by somatic aberrations, TME, and survival, but not by differentiation. The six immune subtypes are wound healing, IFN-g dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-b dominant, all being characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis [84]. Again, the heterogeneous features of tumor-immune cell interactions are mechanisms underlying cell survival, not sustainable development and organ genesis [85]. Interestingly, TME by definition contains the anatomically distinct regions defined as CSC niches that maintain CSCs by preserving their self-renewal, clonal tumor initiation capacity, and clonal long-term repopulation and metastatic potential, as well as by shielding them from immune surveillance [86]. It has been shown that cells within the CSC niches produce factors that stimulate CSC self-renewal, induce angiogenesis, regulate immune cells, and recruit other stromal cells that secrete additional factors to promote  Cell surface Drug resistance, cell migration and stemness [55,56] (continued) tumor cell survival (eg, invasion and metastasis), Conversely, cells composing normal stem cell niches (eg, neural stem cell niches) affect stem cell differentiation in addition to preserving their self-renewal through numerous biophysical and biochemical mechanisms [87]. Presentation of systematical comparisons between CSC niches and those of regular stem cells is beyond the scope of the current work; however, such analytical outcomes will undoubtedly help us to better understand the fundamental biology of CSCs. The data have kept kindling our intention to suggest that the fundamental developmental biology principles should caution application of the stem cell concept in labeling any terminal oncological and pathological cell phenomena [83]. conditioning of glioblastoma cells suggested that stem cell-like tumor initiation cells, regardless of origin, may not be a fixed population of neoplastic cells [30]. Instead, CSC capacities such as expressions of representative markers, metastasis, oncolytic drug resistance and symmetric or asymmetric cell division may likely be a cluster of transient events occurring in a subpopulation of cancer cells when stressed or induced by environmental, epigenetic, genetic, and therapeutic impacts. Thus, the actual number of CSCs existing in a given tumor for a particular time point is determined by the optimal probability of the unilateral survival and growth of the entire tumor [90,91]. 5. CSCs can emerge under varied combinatorial regimens that comprise all the aforementioned scenarios, which is a rational extrapolation that we made.
With the introduction of the fourth and fifth definitions of CSCs, data previously used as evidence to question real existence of CSCs can now be turned into valuable information to suggest an alternative concept. As an example, CSC composition ratio in different tumors might range from 0.2% to 82.5%. Using standardized limiting dilution assays, researchers uncovered that this ratio actually increased in breast cancers along their Stage I to Stage III progression. In contrast to Stage III-IV melanomas, tumorigenic cell ratios remained steadily at around 30% [92]. It has been known that CSCs in the same tumor could carry overlapping, nonoverlapping, or even varied characteristic markers [93,94].

CSCs Are Tumor Survival Cells
In addition to the aforedescribed results, reports showed that the specific molecular mechanisms underlying commonly targeted tumor cell ''stemness'' are unstable. The observations of genetic instability imply a real possibility that different new parental CSC lines may continuously be produced in high-grade malignant tumors. This explains why expressions of some CSC markers are time dependent [95]. With regard to the latter point, an informative comparison case can be made by examining key profiles of ESCs versus those of ECCs that have been traditionally portrayed as opposite sides of the same coin [95].
ECCs have been identified as the ''stem cells of teratocarcinomas'' and as the malignant counterparts of ESCs for mammals. Unlike ESCs that are derived from the inner cell mass of early blastocyst-stage embryos, ECCs are isolated from embryonal teratocarcinomas. It is only after prolonged in vitro culture under certain regimens that some human ESCs (hESCs) start acquiring karyotypic modifications that can be observed in human ECCs (hECCs). Over the chronic incubation process, hESCs can manifest faster proliferation rate and become more maintainable in vitro. Markedly, the more transformed hESCs can form teratocarcinoma-like neoplasms in SCID mice following transplantation. Conversely, the donor hESCderived teratocarcinoma was able to give rise to characterizable hESCs in vitro. It was therefore concluded that hESCs under particular in vitro induction conditions could develop in similar ways that hECCs do during tumorigenesis [96].
Our analysis, based on a crossdisciplinary approach of stem cell biology and developmental neurobiology, suggests that the in vitro transforming process may actually be a journey for ESCs to gradually lose their repertoire of authentic stemness biology, for which ECCs either do not own or are in severe deficiency. This postulation renders the two types of cells not at all belonging to ''opposite sides of the same coin'' (ie, both ESCs and ECCs possess stemness biology). The conclusion is corroborated by findings from more advanced investigations. In a study of the hECC lines, NT2/D1 and NT2/B9, which were clonally derived from a xenograft tumor of the teratocarcinoma cell line Tera-2, extensive differentiation could be induced in vitro by retinoic acid treatment [97]. This differentiation was particularly marked by the disappearance of SSEA-3 that is typically expressed by hECCs. Among the differentiated cell phenotypes, hECC-produced neuron-like cells showed morphological features of neurites and expressed tetanus toxin receptors and neurofilament proteins [97]. But these NT2/N neurons did not further mature into true neurons that could express phosphorylated neurofilament heavy (NF-H) proteins after engraftment in young adult or developmental rodent brains [98]. They nevertheless survived for >12 weeks to >1 year in rat brains under immunosuppression [98,99] and for more than 27 months in a poststroke human brain [99].
The fact that NT2/N cells showed much longer graft survival duration in the brain relative to that of freshly isolated primary neurons or neural progenitor cells indicates that they might have obtained additionally augmented individual survival efficacy, albeit reduction of neural stemness (ie, diminished ability to differentiate into mature neurons) [98][99][100][101]. Accordingly, the NT2/N neurons, not the predifferentiated NT2 progenitor cells, constitutively synthesized intracellular beta/A4 peptide, a major pathologic peptide accumulating in Alzheimer disease (AD) brains, and released it into the cell culture medium [102]. The secreted form (sAPP) of the AD amyloid beta/A4 protein precursor (APP) is a potent player in promoting neurite extension, synaptic formation, overall neurotropic support, and antiexcitotoxicity effect for neuronal cells, as well as in enhancing fibroblast growth [103][104][105][106][107][108]. Clearly, the gain of function in the ECC and ECC-derived neuron-like cells is the selfsurvival capability (eg, production of sAPP, enhanced grafting, etc.). Contrariwise, the loss of function for ECC and ECC-differentiated neuron-like cells is the diminished capacity of authentic stemness (eg, their inability to become mature neurons, defect in functional integration, etc.). Following this novel route of logical reasoning, cautions are deemed necessary when trying to use the NT2/N cells in vitro for investigating NSC and adult neuronal properties, modeling neuronal diseases, or discovering neuronal therapeutics [109,110]. We believe that data extrapolation without factoring the essential cell biology discrepancy could yield misleading information because changes in survival metabolic events may induce alterations of stemness marker presentations. For example, expression of CD9 gene and protein, a cell transmembrane molecule family mediating signal transductions, showed selective upregulation in human glioblastoma stem cell-like cells. CD9 silencing in three CD133+ glioblastoma cell lines triggered amelioration of cancer cell proliferation, survival, invasion, and self-renewal ability through impacting activation patterns of the Akt, MapK, and Stat3 signaling transducers, in which the signaling pathways are mainly involved in cell survival functions, which in turn resulted in expression alternations of CD133, nestin, and SOX2 [45].
Collectively, the metabolic, mitotic, and survival behavioral features as well as the overall life journey endpoints of the currently termed CSCs are vitally different from those of conventionally defined stem cells. Due to the permeating influence of stemness as an established concept that has been academically inscribed for characterizing normal primordial or tissue-specific germ cells, the use of CSC as a nomenclature may cast shadow over conscious and subconscious reasoning of investigators when they aim to tackle malignant essentials of tumor cells. Moreover, the concept of CSC certainly does not hold the original metaphorical implication of stem cells for their capacity to give rise to homeostatic and reproducible multicellular organisms, including human bodies. Therefore, establishing a new nomenclature of tumor survival cell (TSC) to replace CSC appears to be highly valuable since unilateral tumor cell survival essentials consist of endeavors of self-renewal, proliferation, limited differentiation to generate adaptive heterogeneity, migration, metastasis, immune diversity, and drug resistance.
Potential Benefits of Adopting TSC as a New Nomenclature to Replace or Coexist with CSC Application of TSC as an academically and scientifically further justified nomenclature may benefit the intellectual and research fields for its clarity and uniqueness that are tangibilized by the following perspectives.
1. To add special insight to the word ''stem'' when it is used to describe cancer cells for research and therapeutic development [110]. 2. To dissect the definitive difference between TSCs and normal stem cells.
TSCs have pathologically maximized levels of individual and group survival ability and utterly perished capacity for authentic stemness biology. TSCs behave like desperados, tumor cell outlaws that act desperately to survive and appear unstoppable, but they are eventually wiped out in their selfrendered catastrophic circumstances. By contrast, ESCs or tissue-specific progenitor cells maintain a homeostatic equilibrium between its stemness biology and individual survival drive to build a sustainable and reproducible entity.
3. To reveal the lack of real stemness capability in CSCs.
This will help to refocus on unilateral cell survival mechanisms when devising therapies to block cancer metastasis [111]. As a testable hypothesis, what is truly needed for curing the so called CSCs may likely be resurrection of authentic stemness biology and mitigation of the abnormally augmented survival biology inside tumor cells. 4. To emphasize the endpoint of the essential functions of the TSCs that is to carry out desperado-like survival behaviors (DSB). DSB as a novel term of cancer cell oncology describes the state or process of being able to live by all means available, mostly in spite of unfavorable accidents and tribulations, actually in a self-triggered perturbing, stressful, and unsustainable environment. Manifestly, tumor cell DSB do not lead to any real continuity of individual tumor cells or tumor mass as an entity. Through a single spearheaded DSB-driven relentless proliferation, growth, and metastasis, malignant tumor cells will eventually cause the imminent demise of their host organism, followed by reaching the termination of their own journey of survival. Hence, the actual use of the term TSC may further improve the fine-tuning of the direction of our reasoning in search of the oncological prerequisites of these cells in addition to providing the needed demarcation of principal differences between them and the classically entitled stem cells.

Summary
There have been increasingly convincing experimental and clinical data that validate the genetic, epigenetic, and phenotypic heterogeneity of cells comprising malignant tumors. Although questions remain with regard to the consistency and expression levels of specific markers in a subpopulation of cancer cells behaving like stem cell-like tumor initiation cells (ie, CSCs) as well as complexity of CSC oncology, they have not been able to shake the foundation of the concept of CSCs. The current CSC models illuminate tumor generation capabilities of subpopulations of self-renewable and tumor differentiable cells that drive cancer progression via producing oncologic heterogeneity, resistance to oncolytic assaults, and the ultimate death of tumor cells due to host decease. The uncertain oncological feature and conflicting genetic signature of the CSCs (eg, CSC markers mostly are genes in charge of survival, not stemness) suggest a necessity of inaugurating a new nomenclature for the demarcation of the fundamental difference between these cells and physiological stem cells to promote cancer and stem cell research.
What emerged from our analysis is a fine but definitive line between the authentic stemness biology and the newly defined cancer cell survivology. We hereby recommend introducing the term of TSCs (Tumor Survival Cells). Implementation of this proposal may facilitate future work to 1474 TENG ET AL.
focus on investigating key events that have gone awry in TSCs to uncover differences between the three bodies of mechanisms underlying normal stem cell biology, cancer cells, and DSB of CSCs/TSCs, respectively ( Fig. 1 and Table 1). Furthermore, by weighing the characteristics of limited ''developmental hierarchy'' in cancer cells and full-scale ''functional multipotency'' of normal stem cells, researchers may explore consequences of nongenetic variability, rare clones, and clonal dynamics within a particular tumor, as well as between the tumor clones and the host microenvironment. Such undertakings may determine the oncological essentials of TSCs in terms of their endpoint of survival, defects in authentic stemness biology, and the impacts on the host. The findings will reveal crucial targets (eg, stemness defect, stemness resurrection, unchecked survival drive, etc.) for assessing risk-based patient selection for receiving a particular medical procedure and developing efficacious targeted treatment for malignant tumors [10,82,112,113].

Acknowledgments
This paper was generated from the accumulated reading notes of Y.D.T. alongside cross-disciplinary research collaborations with his colleagues and trainees.