Evolutionary Analysis of TCGA Data Using Over- and Under- Mutated Genes Identify Key Molecular Pathways and Cellular Functions in Lung Cancer Subtypes

Simple Summary

Evolution drives the initiation and progression of cancer. This is apparent in the concept of “driver mutations” that initiate cancer and observed in cells of the lineage. Less appreciated is natural selection’s role in conserving genes that are necessary for optimal cancer cell fitness. We identified highly mutated and highly conserved (under-mutated) genes across subtypes of lung adenocarcinoma distinguished by their driver mutations. The subtypes often shared highly mutated genes suggesting common utility in adapting to similar tissue environments. Conversely, conserved genes were subtype specific indicating tight co-adaptation with the initiating driver mutation. Conserved genes were highly expressed compared to those selected for mutations consistent with our hypothesis that they are critical for optimal fitness. Thus, subtype-specific conserved genes reveal variations critical molecular pathways and cellular functions within each tumor subtype. Computer simulations suggest targeting tumor-specific conserved genes may represent a highly effective treatment strategy. More generally, we present an investigative approach that uses evolutionary selection for hypotheses building and to identify genes in which further investigation should yield maximal clinical benefit.

Abstract

We identify critical conserved and mutated genes through a theoretical model linking a gene’s fitness contribution to its observed mutational frequency in a clinical cohort. “Passenger” gene mutations do not alter fitness and have mutational frequencies determined by gene size and the mutation rate. Driver mutations, which increase fitness (and proliferation), are observed more frequently than expected. Non-synonymous mutations in essential genes reduce fitness and are eliminated by natural selection resulting in lower prevalence than expected. We apply this “evolutionary triage” principle to TCGA data from EGFR-mutant, KRAS-mutant, and NEK (non-EGFR/KRAS) lung adenocarcinomas. We find frequent overlap of evolutionarily selected non-synonymous gene mutations among the subtypes suggesting enrichment for adaptations to common local tissue selection forces. Overlap of conserved genes in the LUAD subtypes is rare suggesting negative evolutionary selection is strongly dependent on initiating mutational events during carcinogenesis. Highly expressed genes are more likely to be conserved and significant changes in expression (>20% increased/decreased) are common in genes with evolutionarily selected mutations but not in conserved genes. EGFR-mut cancers have fewer average mutations (89) than KRAS-mut (228) and NEK (313). Subtype-specific variation in conserved and mutated genes identify critical molecular components in cell signaling, extracellular matrix remodeling, and membrane transporters. These findings demonstrate subtype-specific patterns of co-adaptations between the defining driver mutation and somatically conserved genes as well as novel insights into epigenetic versus genetic contributions to cancer evolution.

1. Introduction

We use differences in patterns of Darwinian selection for critical genes in mutant EGFR (EGFR-mut), mutant KRAS (KRAS-mut), and non-EGFR/KRAS (NEK) lung cancers to identify variations in their evolutionary arcs and associated co-adaptations that govern their molecular characteristics [1,2,3]. Following an extensive review of molecular oncology literature, Magalhaes [4] found the majority of human genes can be associated with cancer and concluded: “the challenge is determining which are the key drivers”. Similarly, it is increasingly recognized that epigenetic alterations in the expression of normal, non-mutated genes also contribute to cancer evolution [5]. These essential genes cannot be identified on mutational screens, but theory and mathematical models suggest disrupting their function may be an effective treatment strategy [1].

We hypothesize that discerning evolutionary selection for both mutated and conserved genes within different tumor types can be used to demonstrate clinically valuable therapeutic targets. In evolving populations, each mutation in coding genes is subject to natural selection that determines its subsequent prevalence in a population [1,6]. Various mathematical methods have been applied to the underlying evolutionary dynamics of tumor growth [6,7,8,9,10]. Information theory [6,11] and game theory [1], investigation linking molecular changes to carcinogenesis predicted the mutational frequency of a gene within a tumor or cohort of tumors represents an observable manifestation of intra-tumoral natural selection. Thus, when a mutation increases fitness, the affected cell will proliferate at the expense of less fit cells (“positive” or “directional” selection) so that it is observed more frequently than expected by chance. Mutations that do not alter proliferation (“neutral drift”) will have a prevalence determined by chance based on the underlying mutation rate. These represent the well-known dynamics associated with “driver” and “passenger” mutations [12,13]. To these dynamics, we add mutations that decrease fitness (and proliferation), which will disappear from the population or persist at low frequencies (“negative” selection). Thus, a gene observed to have fewer mutations than expected by chance alone is likely under negative selection because its current normal function optimizes fitness and cannot be improved through mutations.

These dynamics permit an inverse problem approach in which the observed frequency of mutations in a gene compared to that expected by chance alone, provides evidence for the type of selection.

Prior investigations across multiple cancer types have found relatively few genes under stabilizing selection [14,15,16]. However, based on computer simulations, we hypothesized that conserved genes primarily emerge from co-adaption with the salient driver mutation(s). Thus, conserved genes will vary depending on both the tissue of origin (i.e., proliferative constraints to malignant growth in local tissue acting as selection forces) and the initiating genetic mutations during carcinogenesis [17]. The tissue environment and the cancer initiating mutations should influence the ecological value of all subsequent molecular changes in the evolving population of cancer cells [2,6].

In our analysis, we assume that the probability of mutation in each base pair is approximately equal across the genome. This is unlikely to be completely true, but a good starting approximation for our analyses. While some studies ascribe variation in the observed frequency of specific genes to differences in their mutational rate [18,19,20], we note that natural selection alone can result in these observed variations. Furthermore, even if some variations in the mutation rate across the genome do exist, natural selection is always the final arbiter of the prevalence of the mutations based on their contribution to fitness. It is, of course, possible that altered mutation rates in neutral or passenger genes (i.e., one that does not alter fitness) could manifest as a difference in prevalence. However, this should result in consistent increased prevalence in all cancer subtypes. In contrast, we find only a small number of genes with an increased mutation rate in all the lung adenocarcinomas (LUAD) subtypes and no overlap among conserved genes.

Here, we investigate the influences of tissue of origin and initiating driver mutations on evolutionary selection for mutated (directional selection) and non-mutated genes (stabilizing selection) during the evolutionary arcs of lung adenocarcinomas. These tumors must adapt to common growth constraints generated by the lung environment but emerge from different initiating molecular events: EGFR mutation (EGFR-mut), KRAS mutation (KRAS-mut), and no EGFR or KRAS mutation (non-EGFR/KRAS).

By identifying conserved genes, this investigative approach demonstrates molecular pathways and cellular functions most critical for maintaining malignant growth in each LUAD subtype. Prior computer simulations predict that disrupting highly conserved genes can produce therapeutic effects equivalent to targeted therapy for driver mutations [1] so that uniquely conserved genes in each subtype may represent novel and tumor-specific therapeutic targets.

2. Methods

2.1. Gene List Acquisition

We divided the TCGA LUAD cohort and classified patients based on known driver mutations in KRAS (G12, G13, Q61, A146), BRAF (V600, N581, G464, G466, G469, G596, D594), and EGFR (L858, S768, L861, G719, T790, indels in exons 18–21). Samples were excluded if they matched criteria for more than one of these genes. Samples that did not meet any of the mutation criteria were classified as NEK (non-EGFR/KRAS). This resulted in three cohorts: EGFR-mut LUAD (n = 58), KRAS-mut (n = 163) and NEK(n = 313), with a total sample size of 534 patients, (downloaded March 2016) [21].

TCGA data was whole exome sequenced with paired tumor/normal analysis to exclude germ line mutations. For our analysis, we limit the genes and associated mutations to those that alter amino acid sequencing (non-synonymous) or those that alter the number of amino acids in the sequence (truncation, stop-gain, frameshifting indel, splicing). Tumor and normal sequence alignment files were downloaded from the Genome Data Commons, and gene-level depth of coverage was calculated by calculating bases covered by sequencing from the above files across each of the RefSeq coding genes (with 25 base pair flanking regions). A base was considered sufficiently covered if the depth of coverage was ≥ 14 in tumor sample and ≥ 8 in normal samples (as has been previously described: https://www.synapse.org/#!Synapse:syn1695394, accessed on 11 March 2016). The fraction of each gene’s protein coding bases (using the longest RefSeq transcript) covered by sufficient sequence data was calculated for each sample using the Negative Storage Model [22].

Coverage files were downloaded as described in the Synapse page. Gene-level depth of coverage measures the fraction of each gene (longest transcript) covered by sequencing data. To address sequencing artifacts that falsely decrease mutation rates, we excluded genes with low average depth of coverage (< 50%) and errors in the RefSeq gene model.

To minimize potential artifacts related to expression, and to focus on genes that are likely functional, genes were analyzed only if their expression is > 2.0 log₂ in at least one sample (either normal tissue or 1 of the 3 LUAD cohorts). Changes in expression in one or more tumor samples compared to normal tissue were analyzed separately to supplement the genetic data.

2.2. Mutational Frequency

Our approach identifies genes that are mutated more or less frequently than expected based on chance alone. This is established by plotting the observed number of mutations in each gene for each cohort against its size (number of base pairs).

Our primary metric for natural selection was based on whether mutations to a gene were less (stabilizing selection) or more (directional selection) frequent than expected by chance. Assuming the probability of mutation was approximately equal for every base pair in an expressed gene, the background mutation rate was determined by regressing the mutational frequency of each gene within each subtype against gene size (Figure 1a). The distance of each gene to the regression line was then determined, and this standardized residual was compared across subtypes (Figure 1b–d). A negative residual value indicates fewer mutations whereas a positive value indicates more mutations than expected.

Within each subtype, evidence for stabilizing or directional selection was defined as those with a distance from the neutral line > 2 standard deviations below or above the mean, respectively (Supplementary Figures S1 and S3). However, this approach was limited in the EGFR-mut cohort, which was both smaller in size and had a lower overall mutation rate than the other cohorts. Thus, most genes in the EGFR-mut cohort have 0 mutations so that, particularly in small genes, evidence for selection cannot be distinguished from chance alone. Similarly, a single random mutation in a small gene in one cohort member could appear significant.

To include the EGFR-mut cohort in our study, we applied additional metrics. First, we examined the mutational frequency in each cohort reasoning that the value of a mutation can also be estimated based on its observed prevalence in the population (e.g., EGFR-mut is observed in all members of its eponymous cohort). For the purposes of our analysis, we arbitrarily defined mutations under directional selection if observed in > 10% of cohort members (Supplementary Figure S2). The mutational frequency data for each cohort is available in Supplementary Table S1 so that other criteria can be investigated. Similarly, in identifying potentially targetable conserved genes in the EGFR-mut cohort, we reasoned that a conserved gene would be most valuable as a potential target if it was both conserved and highly expressed in all cohort members. Thus, we defined evolutionary conservation as genes with 0 mutations and > 4-fold increased expression in the cohort compared to normal lung tissue (Supplementary Figure S4). For comparison, we also applied these additional metrics to the NEK and KRAS-mut cohorts.

2.3. Expression Data

RNAseq data for each gene in each tumor subtype as well as adjacent normal tissue was obtained from the TCGA database. To avoid artifact due to genes that are not expressed or under-expressed, we analyzed only genes with > 2.0 expression in the tumor or normal tissue.

2.4. Identifying Conserved Pathways and Functions

When investigating groups of genes, curated lists were entered into DAVID (Database for Annotation and Integrated Discovery), available at the website https://david.ncifcrf.gov (accessed on 17 August 2021). Both Gene Ontology (GO)- DIRECT and -FAT were used to identify significant ontologies, including Biological Process (BP), Cell Compartment (CC), and Molecular Function (MF). We then performed functional annotation clustering. We selected clusters based on their being many genes or based on biological significance. Finally, to identify genes in major pathways, KEGG pathways were searched using DAVID. Venn diagrams were constructed using the Ghent University VIB/UGent Center website: http://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 1 September 2021).

In identifying key interactome pathways, we assumed cancer cells would be highly intolerant to such disruptions and we thus considered a gene to be conserved if it had 0 mutations in members of the entire subtype and had normal or increased expression in LUADs.

3. Results

3.1. Cohort Demographics

Demographic details were missing or incomplete in about 20% of members in each cohort. The available data are shown in Supplementary Table S5. Patient age and tumor stage at diagnosis were not significantly different in the 3 cohorts. Consistent with prior studies, patients with EGFR-mut lung cancer were far more likely to be female and non-smokers than in the other cohorts.

3.2. Gene Expression and Evolutionary Selection

Prior studies in yeast and the human germ line [23] have suggested an increased mutation rate in highly expressed genes concluding that transcription is associated with DNA damage. Here, we find the opposite—highly expressed genes are also highly conserved (Figure 2). This is evolutionarily sensible because highly expressed genes are likely to be critical for optimal cell fitness and, therefore, more likely to be subjected to evolutionary conservation. Supporting this, we find (Figure 2) highly conserved genes rarely show a significant change in gene expression (compared to normal lung tissue) suggesting they are simply maintaining their usual function while genes under evolutionary selection for mutations frequently demonstrate increased or decreased expression (perhaps related to gain or loss of function mutations).

3.3. Highly Mutated Genes under Directional Selection

The role of gene size (number of base pairs) in its observed frequency has been noted in several publications. Genes that are observed to be frequently mutated are often labeled drivers, while others [24] propose this results in “false positives” in larger genes in which the increased mutation is simply the result of the larger number of base pairs subjected to random mutation, Thus, for example, genes such as CSMD1 and MUC16 have been labelled cancer drivers and false positives. Here, we show (Supplementary Table S1) that despite their large size, both genes are far (> 2 standard deviations) from the neutral line and thus can be appropriately labeled drivers. In contrast other large genes such as MLL2 and LRP2 are frequently mutated in the KRAS-mut and NEK LUADs but not more than expected based on size and are thus under neutral selection.

Since our focus is primarily evaluation of conserved genes (stabilizing selection), here we present only those over-mutated genes found in > 10% of tumor subtype samples. As has been previously noted, the number of observed mutations expected by chance alone will depend on the mutation rate and gene size. This is summarized in Supplementary Figure S1 in which we compare the frequency with which a gene is mutated in relation to its distance from the neutral line. Thus, all frequently mutated genes in the EGFR-mut cohort are > 2 standard deviations above the neutral line indicating positive (directional) evolutionary selection. In the other 2 cohorts, most, but not all of the frequently mutated genes, are also under directional evolutionary selection. Other mutations under strong directional selection can be found in Supplementary Figure S1. By this criterion, EGFR-mut cancers, consistent with the cohort’s overall decreased mutation rate, have only 10 gene mutations under directional selection compared to 153 and 205 for KRAS-mut and NEK, respectively. Among these highly mutated genes, all are frequently mutated and, using the criterion of > 10% prevalence in the cohort, 7 are common to all cohorts, and 2 are common to EGFR-mut and NEK. In addition, 109 are common to KRAS-mut and NEK so that only 1, 37, and 87 highly mutated genes are unique to EGFR-mut, KRAS-mut, and NEK tumors, respectively.

The highly mutated genes common to 2 or 3 cohorts include well-known genes (e.g., TP53, STK11) and others not extensively investigated. Although EGFR-mut had only 10 gene mutations in > 10% of cohort members, all 10 (TP53, MUC16, CSMD1, RYR2, FLG, PCLO, AHNAK2, GRIN2A, PKD1L1, LAMB4) show strong directional selection in the other subtypes (although not all met the > 10% criteria; Supplementary Table S1). These are enriched for genes associated with Ca²⁺ signaling including ryanodine (RYR2) and glutamate (GRIN2A) receptors. The critical role of calcium dynamics in LUADs is evident in other common mutations including PCLO, associated with calcium channel CaV1.2 [25]; AHNAK, with Ca²⁺ voltage gated channels; and PK1D1, which regulates Ca²⁺ dynamics in cilia [26]. In addition, CSMD1, a membrane bound complement inhibitor, and FLG and MUC16 (CA125) reflect directional selection on genes associated with extracellular inflammation and matrix (ECM) components (see below).

3.4. Evolutionarily Conserved Genes as Evidence for Stabilizing Selection

We identified 22, 160, and 248 conserved genes using the criterion of > 2 standard deviations below the neutral line in the EGFR-mut, KRAS-mut, and NEK subtypes, respectively (Supplementary Figure S3). By this metric, no conserved genes were common to all three subtypes and only 14 were found in more than one subtype including REV3L in EGFR-mut and KRAS-mut; HTT and VPS13D in EGFR-mut and NEK; MDN1, SPATA31A7, NBPF10, LRRC37A2, NPIPB5, NBPF11, SPATA31C2, ZNF729, RGPD6, RIMBP3, and CACNA2D2 in KRAS-mut and NEK. Interestingly, most of these conserved genes have not been extensively investigated in LUAD, although some have been identified in broad molecular cancer risk and prognosis studies [27,28,29]. Nevertheless, our conservation and expression data suggest these genes have been activated and are useful and perhaps essential for their normal, unaltered function.

Conservation of two members of the NBPF (neuroblastoma break point) and SPATA (spermiogenesis associated ATPase) families show a pattern where genes under stabilizing selection have no known role in normal lung tissue while being associated with other tissues particularly the brain and testes. This pattern is evident in the other common conserved genes: RIMBP3, associated with manchette function and spermiogenesis [30], HTT, mutated in Huntingtin’s disease [31] (although a reduced incidence of cancer is reported in Huntingtin’s disease [32]), CACNA2D, associated with Parkinson’s disease [33], and VPS13D, associated with ataxia [34] with a possible role in mitochondrial fission and peroxisome biogenesis [30,35].

We identified 446, 160, and 66 genes with no mutations and > 4-fold increase in expression for EGFR-mut, KRAS-mut and NEK subtypes, respectively (Supplementary Figure S4). Only 15 genes meeting these criteria were common to all subtypes. Some have known roles in LUAD including: SLC2A1 (GLUT1) [36], RHOV [37], GJB2 [38], S100A2 [39], MB [40], BARX2 [41], and CDKN3 [42]. This supports further investigation of the other conserved genes in this category (SPINK1, FGF11, CCNB2, XAGE1, PRSS3, DDIT4L, FDCSP) which are not known to have a role in LUADs.

3.5. Evolutionary Selection on Cellular Pathways

Sustained proliferation of cancer cells depends upon persistent delivery of oncogenic signals from driver genes. This requires both gain of function mutations in the driver and evolutionary conservation of the molecular circuitry that transmits the signal to other cellular components. Importantly, different evolutionary dynamics will be observed for cancers initiated from mutations of tumor suppressors. Here, absence of signaling due to loss of function mutations in a tumor suppressor eliminates selection pressure on the downstream molecular wiring. As a result, these genes will exhibit a neutral evolution.

Accordingly, the pattern of mutations in the interactomes of driver and tumor suppressor genes can provide insights into critical signaling pathways in LUAD subtypes.

3.6. TP53 Interactome in Lung Cancer

TP53 is active in multiple different cellular pathways and the most frequently mutated gene in LUAD. It is usually viewed as a tumor suppressor requiring a loss of function mutation. As noted above, following a loss of function mutation, the molecular wires in the interactome should exhibit a neutral pattern of mutations. This is, in fact, observed in some genes within the TP53-mut interactome (Supplementary Table S3). However, there is consistent conservation (under-mutated) in the interactome genes associated with senescence, apoptosis, invasion, DNA repair, metabolism, and mitosis. This suggests mutant P53 may retain some functions that promote the proliferation and survival of the cancer cells, consistent with some recent experimental observations [43].

3.7. EGFR-Mut and KRAS-Mut Signaling

Following a gain of function driver mutation, we expect strong stabilizing selection for the molecular wires that carry the oncogenic signals and downstream effectors that perform the necessary functions. Interestingly, within KRAS-mut interactome [44], just 2 genes, WDR20 and VT1B, (Supplementary Table S4) have 0 mutations, whereas 11 genes in the EGFR-mut interactome, ENGASE, NDUFA4, AP2A1, AP2A2, AP2B1, AP3M1, ERBB2, CCDC37, DNAJA2, GRB2, and HSPA5, have 0 mutations. In total, conserved members of the interactome and downstream effectors in the EGFR-mut interactome include 259 protein-coding genes in its eponymous group compared to 34 in the KRAS-mut group.

3.8. Evolutionary Selection on Cellular Functions

Genes identified as under natural selection by the above criteria are shown in Supplementary Figures S1–S4. Here, because of space limitations, we present brief analysis of some broad cellular properties.

3.9. Evolutionary Selection on Signaling Pathways

In the EGFR-mut tumors, 7 members of the MAPK pathway are under stabilizing selection (

Table 1. Evolutionary selection on genes related to signaling pathways.

Evolutionary Selection for Mutations or Conservation in Intracellular Pathways
EGFR-Mut		KRAS-Mut		NEK
Conserved	Mutation	Conserved	Mutation	Conserved	Mutation
MAPK
HRASLS	MAP3K13	MAP3K6	MAPK7	MAP3K1	FAM83C
EGF	MAP7D2	MEGF11	FAM83B	MAP4K5
	MAP2K4	VEGFC	INAVA	MEGF9
	MAP3K11
	MAP3K9
	RASA2
	RASAL2
	RASL10B
MYC
HECTD4
NOTCH
			NOTCH4		NOTCH4
Rho receptors
RHOV	RHOH	RHOV		RHOV
Rho/Rac guanine nucleotide exchange
ARHGEF19	ARHGEF40	ARHGEF5	ARHGEF6	ARHGEF1
	ARHGAP6	ARHGAP36	ARHGAP6	ARGHEF39
		KIAA0355 (GARRE1)		ARHGEF39
Rab pathway
RAB26		RAB3GAP1		RAB3B
RAB3GAP2		RAB26
		TBC1D2B		TBC1D3H
				TBC1D3C
		DENND4C
		DENND5A
			FAM71B GARIN3		FAM71B GARIN3
		ST5
Rac pathway
				RAC3
RAN pathway
				RANBP9
Protein tyrosine phosphatase receptors
PTPRH	PTPRN	PTPRF	PTPRD		PTPRD
			PTPRT		PTPRT
			PTPRN		PTPRZ1
ERBB
	ERBB4		ERBB4
Interleukin receptors
IL36RN	ILRL1	IL36RN		IL1R1
IL23A	IL1RAPL2	IL17C		IL27RA
IL37				IL23A
IL31RA
IL22RA2
IL1RL2
IL41L
IL36G
Interferon
				IRF2BP1
				IRF5
WNT/Catenin
		WNT3
			CTNNA2		CTNNA2
			CTNND2		CTNND2
			FAM123B (AMER1)		FAM123C (AMER3)
Lipid Receptors
	NLRP3		NLRP3		NLRP3
	NLRP14		NLRP14		NLRP14
	NLRP5		NLRP5		NLRP3
			LLRP1B		LRP1B
			NLRP10
					LRP1B
			LRP1B
HIPPO
		WWC2
NOTCH
			NOTCH4		NOTCH4
Fibroblast Growth Factor
FGF11		FGF11		FGF11
		FGF19
Insulin like growth factors
IGF2BP3		IGFL2		IGFL2
				IGFBP3
Toll Like Receptors
			TLR4		TLR4
Ephrin Receptors
EPHX4	EPHA3	EPHA1	EPHA3	EPHA5	EPHA3
EPHB2	EPHA6	EPHB6	EPHA6		EPHA6
			EPHA5		EPHS5
			EPHB6
			EPHA3
EFNA4		EFNA4
EFNA3				EFNA3
Ryanodine Receptors
RYR3	RYR2		RYR2		RYR2
			RYR3		RYR3
			RYR1		RYR1
Glutamate Receptors
	GRIN2A		GRIN2B		GRIN2A
					GRIN2B
			GRID2		GRID2
Semaphorin
	SEMA5A				SEMA5A
	SEMA5B				SEMA5B
G-Coupled Protein
GPR115	GPR158	GPR113	GPR112	GPR108	GPR112
GPR87			GPR148	GPR19	GPR158
GPR110				GNL	GPR141
					GPR174
					GPR139
					GPR98
					GPR158
Inositol
ITPKA				ITPKA
cAMP signaling
	ADCY2		ADCY2		ADCY2
	ADCY8				ADCY8
					ADCYAP

). HRASLS, a wild type KRAS effector [45] and possibly a Ca²⁺-independent N-acyltransferase [46], is conserved. A notable surprise is conservation of EGF with > 4-fold increased expression only in EGFR-mut LUAD. We can find no prior literature on this topic but our results suggest EGF may contribute to the fitness of these lung cancer cells perhaps though complementary signaling circuits. Cancer cells in both KRAS-mut and NEK groups conserved members of the MEK family while WT tumor also conserved MAP4K5, which does not have a known role in LUAD. KRAS-mut and NEK tumors conserved members of the MEGF family, which have not been extensively investigated. KRAS-mut cancers conserved VEGFC, an activator of lymphangiogenesis and immunomodulator associated with poor prognosis in LUAD [47].

In all 3 LUAD subtypes, RHOV is conserved (using the 0 mutations and > 4-fold increased expression criteria). Other conserved genes common to all three LUADs involve members of the Rho Guanine Nucleotide Exchange family, components of the RAB pathway, Interleukin, Ephrin, fibroblast growth factor, and G-couple protein pathways. NEK tumors conserved elements of interferon signaling. All subtypes exhibited directional selection for mutations in lipid receptors.

Evolutionary divergence between LUADs provides evidence for distinct co-adaptations between the initiating driver mutations and subsequent evolution. For example, RYR3 was highly mutated in KRAS-mut and NEK tumors but conserved in EGFR-mut tumors. While RYR2 exhibits directional selection in all cohorts, RYR1 was highly mutated in just KRAS-mut and NEK tumors. In Ephrin receptors, EFNA4 was conserved in EGFR-mut and KRAS-mut tumor while EFNA3 was conserved in EGFR-mut and NEK tumors. EGFR-mut and KRAS-mut conserved TMEM184A, a heparin receptor that may regulate angiogenesis [48].

3.10. Evolutionary Selection on DNA Repair, Phenotypic Plasticity, and Epigenetic Modifications

Consistent with the low number of average mutations per sample, we find (

Table 2. Evolutionary selection on genes related to phenotypic plasticity and epigenetic modifications.

Evolutionary Selection of Genes Associated with DNA Repair, Phenotypic Plasticity, and Epigenetic Modifications
EGFR-Mut		KRAS-Mut		NEK
Conserved	Mutation	Conserved	Mutation	Conserved	Mutation
Histones
HIST1H2AM		HIST1H2AM
HIST1H2BD		HIST1H2BD
		HIST1H2BG
Acetylation/Methylation
CDYL2	HDAC9		HDAC9	CDY1	HDAC9
ACY3				CDY1B
BRD4				CDY2A
B4GALNT44				CDY2B
GCNT3				CLOCK
ST8SIA2				HDAC5
				HEXB
				GCNT3
				HDAC5
		KIAA0182 (GSE1)
Cytochrome P450 family
CYP2D6					CYP11B2
CYP27B1					CYP11B1
CYP27C1					CYP26B1
CYP241
					ABCB1
Homeobox/RNA Polymerase II
BARX2		BARX2		BARX2
ETV4		ETV4
SIX4		SIX1
SIX2
HOXC10	HOXA13	HOXB9	HOXA13
HOXB9	HOXA1	HOXC10			HOXA1
HOXC13	HOXA3	HOXB3			HOXA3
HOXC9		HOXA10			HOXA5
HOXB13
HOXD4
HOXD3
HOXB8
HOXC11
HOXC8
HOXC6
HOXB7
FOXM1	FOXG1		FOXG1		FOXF2
FOXP3
FOXB1
ONECUT1		ONECUT1
ONECUT2		ONECUT2
OTX1
PAX7					PAX4
PAX9
PITX2		GREM1
E2F2
E2F8
		ASXL1	ASXL3		ASXL3
POU3F2	POU3F4				POU3F4
		CIITA
		NOMO2		NOMO3
			SATB2		SATB2
			TSHZ3		TSHZ3
			ZEB1		ZEB1
			ZFHX4		ZFHX4
Multicellularity
PLXNB3	SEMA5A				SEMA5A
	SEMA5B			SEMA4B	SEMA5B
					SEMA6D
FREM2					SEMA3D
	ABCB5		ABCB5		ABCB5
					ABCB1
			GLI2
GINS1		GINS1
GINS2		GINS2
E2F transcription factor 5
E2F8
E2F2
Cyclin-dependent genes
CDKN3	CDKN2A	CDK13	CDKN2A	CDK5RAP1	CDKN2A
		CDKN3		CDKN3
		CDK6		CDK5R2
		CDKL2		CDK5R2
Telomere
		TERT
Zinc Finger proteins
ZFHX3	ZFPM2	ZNF729	ZNF479	NF729	ZNF479
	ZFHX4	ZNF827	ZNF536	ZNF324	ZNF536
		ZNF839	ZNF676	ZNF84	ZNF676
		ZNF687	ZNF804B	ZNF726	ZNF804B
		ZNF845	ZNF385D	ZNF749	ZNF385D
		ZC3H4	ZNF208	ZNF642	ZNF208
		PDZD8	ZNF257	ZNF30	ZNF257
		ZZEF1	ZNF716	ZNF57	ZNF716
			ZNF521	ZNF433	ZNF521
			ZNF831	ZNF655	ZNF831
			ZNF98	ZNF503	ZNF98
			ZNF804A	ZNF846	ZNF804A
			ZNF711	ZNF205	ZNF648
			ZNF679	ZNF26	ZBTB1
			ZNF835	ZFPM2	ZBBX
			NF479	TSHZ3	ZBTB1
			GLI3	ZFP106	ZFPM2
			ZEB1	ZBTB1	TSHZ3
			ZFPM2	ZDHHC5	ZEB1
			TSHZ3	TSHZ3	ZCCHC5
			ZIC4		ZIC4
			ZIC1		ZIC1
			ZSCAN4		ZIM2
			ZCCHC12		TSHZ2
			ZIC3		ZFHX4
			ZSCAN1
			ZSCAN5B
			ZFHX4
DNA repair
BRCA1
BRCA2
KIAA0101 (PCLAF)		KIAA0101 (PCLAF)
REV3L		REV3L
XRCC2
CHAF1B
CLSPN
EME1		EME1
EXPO1
FOXM1
MCM10
TONSL
UBE2T		UBE2T	UBE2A
UBE2C
EXPO1
HROB
				AUNIP

) that the EGFR-mut cancers conserved 16 genes related to DNA repair (e.g., BRCA1 and BRCA2). In contrast, we identified only 1 and 3 conserved genes associated with DNA repair in the NEK tumors and KRAS-mut groups, respectively, suggesting a classic “mutator phenotype”. We hypothesized the role of epigenetic mechanisms for phenotypic plasticity will vary inversely with the mutator phenotype. Consistent with this, we found that EGFR-mut cancers conserved more homeobox genes and genes related to RNA Polymerase II compared to KRAS-mut and NEK cancers (Table 2). In contrast, NEK tumors showed greater selection for mutations in homeobox and RNA Polymerase II genes. Interestingly, NEK tumors uniquely conserved 4 members of the CDY family, which contain a chromodomain and a histone acetyltransferase domain. One prior study found high expression of CDYL correlated with poor survival in NSCLC [49]. We find directional and stabilizing selection for genes involved in z-finger proteins in KRAS-mut and NEK, but not EGFR-mut, subtypes.

3.11. Evolutionary Selection on Microenvironmental Interactions

Extracellular Matrix (ECM)-related genes were variously under both stabilizing and directional selection in lung cancer (

Table 3. Evolutionary selection in genes associated with the extracellular matrix.

Extracellular Matrix
EGFR-Mut		KRAS-Mut		NEK
Conserved	Mutation	Conserved	Mutation	Conserved	Mutation
Matrix Metalloproteinase
MMP17			MMP16		MMP16
MMP9			MMP2
ADAMTS
	ADAMTS5	ADAMTS8	ADAMTS16		ADAMTS16
	ADAMTS14		ADAMTS2		ADAMTS2
			ADAMTS20		ADAMTS20
			ADAMTS12		ADAMTS12
			ADAMTS18
Membrane anchored proteases
TMPRSS4	TMPRSS12	TMPRSS4	TMPRSS11E
TMPRSS11E	TMPRSS15		TMPRSS15
Bone Marrow Morphogenetic Proteins
BMP8A	BMP1
	FAM5C (BRINP3)		FAM5C (BRINP3)		FAM5C (BRINP3)
					FAM5B (BRINP2)
Protease inhibitors
CST1		CST1
CST2		CST2
CST4
SPINK1		SPINK1		SPINK1
SPINK13		SPINK13
SPINK2
	SERPINA7		SERPINB4		SERPINB3
	SERPINI1		SERPINA5
Collagen
COL1A1	COL6A2		COL6A2	COL4A3BP	COL22A1
COL9A2	COL7A1		COL22A1		COL19A1
COL10A1	COL23A1		COL19A1		COL25A1
COL24A1			COL25A1		COL3A1
			COL3A1		COL11A1
			COL11A1		COL5A2
			COL21A1		COL14A1
			COL1A2		COL6A3
			COL6A3		COL3A1
					COL6A6
					COL12A1
Tenascin
	ODZ1		ODZ1		ODZ1 (TENM3)
	ODZ2		ODZ3		ODZ3
	TNR		TNR		TNR
			TNN		TNN
Other ECM components
	FLG		FLG		FLG
			FLG2		FLG2
Mucin
MUC21	MUC17		MUC16		MUC17
MUC13	MUC16				MUC7
					MUC5B
Laminin Beta Subunit
	LAMB4
Protocadherin
	PCDHB2		PCDHB2	PCDHGB5	PCDHB2
	PCDH15		PCDH15		PCDH15
	PCDHGA2		PCDHGA2		PCDHGA2
	PCDHB7		PCDHB7		PCDHB7
	PCDHGB2		PCDHGB2		PCDHB11
	PCDHA4		PCDHA4		PCDHA2
	PCDHGB3		PCDHGB3		PCDHB4
	PCDHB8		PCDHB11		PCDH11X
	PCDHA1		PCDHA2		PCDH10
	PCDHGA3		PCDHB4		PCDHA3
	PCDHGA7		PCDH11X		PCDH178
	PCDHGA1		PCDH10		PCDHB12
	PCDHGB4		PCDHA3		PCDH18
	PCDHGC5		PCDH178		PCDHB14
	PCDHGC4		PCDHB12		PCDH11Y
	PCDHGA10		PCDHA6		PCDH8
	PCDHGA5				PCDHB3
	PCDHGA9				PCDH17
	PCDHGA12
	PCDHGA4
	PCDHB6
	PCDHGB
	PCDH19
	PCDHGB5
	PCDHGA8
	PCDHGA11
	PCDHGB6
	PCDHGC3
	PCDHGA6
	PCDHGB1
Cadherins
CDH3	CDH10		CDH10		CDH10
CDH17	CDH6		CDH18		CDH6
	CDH13		CDH9		CDH18
	CDHR1		CDH22		CDH9
	CDHR2		CDH7		CDH22
	CDH18		CDH8		CDH7
	CDH23		CDH2		CDH12
			CDH11
Atypical cadherins
FAT4			FAT3		FAT3
			FAT4		FAT4
					FAT1
Myosin
MYO7A	MYO7B			MYO5C	MYO7B
	MYOD1				MYO18B
	MYO18A
	MYO1G
Perlecan proteins
HSPG2
Serine/threonine kinase
STK32A			STK11		STK11
Intergrins
		INHA
		IBSP

). Significant differences in the subtypes suggests that adaptations for modulating the microenvironment are determined by the driver mutations. For example, 8 collagen (COL) family members are either conserved or highly mutated in EGFR-mut cancers. In KRAS-mut and NEK cancers no genes of the COL family were conserved, while 9 and 11 genes were highly mutated, respectively. All tumor types exhibited strong directional selection for mutations in members of the cadherin and protocadherin families. NEK tumors conserved a single protocadherin family member (PDCHGB5) and EGFR-mut tumors conserved CDH3 and CDH17.

Mucins are known to evolve in adenocarcinomas and can form complexes that alter signaling circuits [50]. Accordingly, mutations in MUC16 (CA125) were selected for in EGFR-mut and KRAS-mut cancers while only mutations in MUC17 were selected in NEK. MUC21 and MUC13 were conserved only in EGFR-mut cancers.

Proteases are necessary for ECM remodeling and EGFR-mut cancers conserved 3 members of the MMP family. In contrast, no members of the MMP were conserved in KRAS-mut and NEK cancers but several member genes were highly mutated. Directional selection for mutations were observed in multiple members of the ADAMTS family in all 3 LUADs. Only in KRAS-mut cancers was a single family member, ADAMTS8, conserved. EGFR-mut and KRAS-mut cancers, but not NEK cancers, had both stabilizing and directional selection among genes of the membrane protease family TMPRSS. Finally, members of the CST and SPINK protease inhibitor families were conserved across subtypes. By way of divergence in co-adaptations, EGFR-mut and KRAS-mut tumors conserved CST1 and CST2 while the NEK tumors conserved CST6 and CST7.

3.12. Evolutionary Selection on Membrane Proteins

We hypothesized that conserving and upregulating genes associated with membrane transporters would enhance cancer cell fitness by increasing substrate harvest rates as well as providing useful information on substrate availabilities. Common to all subtypes (

Table 4. Evolutionary selection in genes associated with cell membrane.

Evolutionary Selection for Mutations or Conservation of Genes Associated with the Cell Membrane
EGFR-Mut		KRAS-Mut		NEK
Conserved	Mutation	Conserved	Mutation	Conserved	Mutation
Claudin
CLDN3	CLDN15	CLDN1
CLDN2
CLDN6
CLDN9
Gap junctions
GJB2		GJB2		GJB2
GJB6		GJB6
		GJB3
Spectrin family
SPTBN5
SPTBN2
Cilia
DNAH10			DNAH11		DNAH11
			DNAH9		DNAH9
					DNAH8
					DNAH3
					DNAH7
					DNAH5
				BBOF1
		KIAA0753
		IFT122
SPAG5	SPAG11B			SPAG4
SPAG9	SPAG4
NEK2				NEK2
	C1orf174 ERICH3		C1orf174 ERICH3		C1orf174 ERICH3
C16orf59 (TEDC2)		C16orf59 (TEDC2)		C16orf59 (TEDC2)
Ca++ Voltage gated channels
CBARP	CACNA2D1	CACNA2D2	CACNA2D1	CACNA2D2	CACNA2D1
	CACNA1E		CACNA1E
	CACNA1C		CACNA1C		CACNA1C
			CACNA2D3
K+ Voltage gated channels
KCNQ5	KCNK2	KCNN4	KCNK2	KCNS1	KCNK2
	KCNA5		KCNA5	KCNC3	KCNA5
	KCNH1		KCNH1		KCNK10
	KCNH8		KCNJ18		KCNT2
	KCNH5		KCNA4		KCNK13
			KCNB2
			KCNJ12
			KCNJ3
			KCNU1
			KCNC2
			KCNJ2
			KCNS2
			KCNQ2
K+ channel tetramerization domain-containing family
		KCTD19			KCTD8
Hyperpolarization-activated cyclic nucleotide-gated potassium channel
			HCN1		HCN1
Na+ channels
			SCN3A		SCN3A
					SCN2A
Sodium leak Channel, non-selective
			NALCN		NALCN
Acid sensing channel
					ASIC2
Glutamate-gated ion channel
	GRIN2A				GRIN2A
			GRIN2B		GRIN2B
					GRIN3A
Cancer-testis genes
XAGE1B		XAGE1B		XAGE1B
XAGE1D		XAGE1D		XAGE1D
	MAGEH1		MAGEC1	MAGED4	MAGEC1
	MAGED2		MAGEC3	MAGED4B	MAGEC3
	MAGED1		MAGEC2	MAGEA2	MAGEC2
			MAGEB4		MAGEB4
			MAGEA10-MAGEA5		MAGEB18
			MAGEB16
			MAGEA5
			MAGEA6
			MAGEA4
CXorf61 CT83		CXorf61 CT83		CXorf61 CT83
Spermatogenesis
			FAM75D1 (SPATA31)		FAM75D1 (SPATA31)
		FAM75A4 (SPATA31A)		FAM75A4 (SPATA31A7)	FAM75A6 (SPATA31A6)
		FAM75C2 (SPATA31C2)		FAM75C2 (SPATA31C2)
					FAM75A5 (SPATA31A5)
	C15orf2 (NPAP1)		C15orf2 (NPAP1)		C15orf2 (NPAP1)
	CXorf59 (CFAP47)		CXorf59 (CFAP47)		CXorf59 (CFAP47)
Fucosyltransferases
FUT2				FUT4
FUT9				FUT2
FUT3
FUT6
Solute Carriers
SLC1A7	SLC5A11	SLC2A1	SLC6A5	SLC1A4	SLC8A1
SLC2A1	SLC6A13	SLC7A5	SLC6A15	SLC2A1	SLC10A2
SLC6A11	SLC6A17	SLC7A10	SLC8A1	SLC7A8	SLC12A1
SLC6A3	SLC10A1	SLC24A1	SLC8A3	SLC7A11	SLC17A3
SLC7A10	SLC34A2	SLC26A8	SLC9A4	SLC19A1	SLC17A6
SLC15A1	SLCO1B1		SLC17A6 SLC17A6	SLC22A5	SLC35F1
SLC16A9	SLCO4A1		SLC27A6	SLC34A3	SLC39A12
SLC29A4			SLC30A10	SLC44A2	SLC44A5
SLC52A1			SLC39A12	SLC44A4
SLCO5A1
Transmembrane proteins
TMEM184A	TMEM200A	TMEM184A	TMEM200A	TMEM180	TMEM196
TMEM63C				TMEM201	TMEM132
TMEM156
TMEM59L
ATP-Binding Cassettes
ABCA4	ABCB5		ABCB5	ABCF1	ABCB5
ABCC3			ABCA13		ABCB1
		ATP2A1			ABCA13
		ATP8B3
		ATP2A2

) was stabilizing selection for transporters of glucose (SLC2A1 [GLUT1]) and neutral amino acids (SLC7 family). EGFR-mut cancers conserved 2 members of the SLC6 family which transport dopamine. NEK cancers conserved 2 members of the SLC44 family which transport choline. Of note, EGFR-mut cancers conserved transporters for monocarboxylate and riboflavin. KRAS-mut cancers conserved a Ca⁺⁺ transporter and anion transporter (SLC26A8) thought to be expressed only in sperm. NEK cancers conserved additional transporters for neutral amino acids, folate, carnitine, and phosphate.

Evolutionary changes to cell membranes and mitochondrial [51] transmembrane potential are frequently seen in cancers [52]. Accordingly, mutations in calcium voltage dependent channels are under strong directional selection (Table 4) in all 3 LUADs while NEK and KRAS-mut tumors conserved CANNA2D2. Interestingly, the multifunctional, Ca⁺⁺-binding S100A2 gene was one of few genes conserved in all 3 cohorts and S100P was conserved in the NEK and KRAS-mut cancers.

In contrast, there is extensive selection for mutations in K⁺ voltage dependent channels across the cohorts with KCNQ5 conserved in EGFR-mut, KCNN4 in KRAS-mut, and KCNS1 and KCNC3 in NEK tumors.

Interestingly, all 3 cohorts conserved the gap junction (by 0 mutations and > 4-fold increased expression) gene GJB2. GJB6 was conserved only in EGFR-mut and KRAS-mut cancers while the latter also conserved GJB3. Increased expression of GJB2, which primarily functions as an ion channel [53], is associated with a poor prognosis in LUAD [38]. GJB6, which encodes connexin 30, is often up-regulated in early stage LUADs [54].

All 3 cohorts demonstrated directional selection for genes in the cancer-testes MAGE and XAGE families. The high prevalence of genetic alterations to cancer-testes genes has been noted, although their precise roles remain undetermined [55].

Finally, we note strong evolutionary selection on ABC efflux pumps, which can confer resistance to treatment, even at the time of presentation (i.e., prior to therapy) suggesting pre-treatment evolutionary dynamics may confer de novo treatment resistance.

4. Discussion

Here, we identify natural selection by establishing a mutation rate in each LUAD subtype through a linear regression that establishes an expected number of mutations in each expressed gene based on the gene size and the mutation rate in each cohort. A gene in which the observed number of mutations is significantly higher or lower than this is then likely to be under positive selection or negative selection. Thus, for example, CSMD1 and CSMD3 are large genes that have been identified as “drivers” in LUADs but also labeled as “false positives” due to their size. Here, we demonstrate the increased mutational frequency is greater than expected even accounting for their large size indicating evolutionary selection. In contrast, other large genes (e.g., MLL2, LRP2) are frequently mutated but do not fulfill criteria for evolutionary selection.

We note that our methodology assumes a roughly equal probability for mutations in all base pairs and, therefore, differs from prior studies that propose regional variations in mutation rate throughout the genome [24]. We acknowledge the technical excellence of this approach but also note it does require assumptions (e.g., all cancer cells have replication time identical to that of HeLa cells) and methods (e.g., using expression data from the average of 91 cell lines) that may limit application.

Thus, for example, this approach finds genes with increased expression have increased mutation rate, perhaps due to the biomechanical stress of frequent transcription. We find the opposite—genes that are highly expressed are more commonly conserved (Figure 2). Evolutionarily, this is sensible as highly expressed genes are likely critical for optimal fitness and thus more likely to be subjected to evolutionary conservation. This conclusion is supported by the observation (Figure 2) that conserved genes are less likely to significantly change expression compared to those in which mutations are highly selected. However, we acknowledge that other co-variates [56] could produce intra-genomic variations in mutation rate that may introduce errors in our investigation.

Our data demonstrate that initiating genetic events (EGFR-mut, KRAS-mut or variable in NEK) influence the subsequent pattern of stabilizing and directional selection on other genes and molecular pathways consistent with driver-dependent coadapted syndromes for each cancer type.

We find substantial but incomplete overlap among the cancer subtypes in the highly mutated genes suggesting directional selection to overcome a relatively fixed number of growth constraints in lung tissue. In contrast, conserved genes are rarely shared among the driver-gene-defined subtypes suggesting fitness contributions unique to each subtype. Primary roles of conserved genes appear to include: 1) Maintaining the molecular wires and downstream effectors for oncogenic signals from driver mutations; 2) Stabilizing selection for molecular pathways and cellular functions which represent convergent and divergent evolutionary dynamics.

We find EGFR-mutant lung cancers require few additional mutations to evolve a malignant phenotype. One plausible hypothesis is that any lung cancer must overcome a relatively fixed number of growth constraints common to all lung tissue. Conservation of multiple genes in the relatively large EGFR-mut interactome suggests broad propagation of its oncogenic signals reduces the need for additional tumor promoting mutations. In contrast, the KRAS-mut interactome is both smaller and has fewer conserved genes suggesting KRAS-mut cancers must compensate for limited oncogenic signal propagation through an increased number of additional tumor-promoting mutations. In other words, a lung cancer requires an EGFR mutation plus X addition gene mutations or a KRAS mutation plus Y additional gene mutations where Y > X. WT tumors, lacking any driver genes, require still more mutations (i.e., > Y mutations) to generate a malignant phenotype. We note this hypothesis leads to a clinical prediction: the large size of the conserved EGFR-mut interactome is consistent with clinical observations that targeted therapy blocking the EGFR-mut oncogenic signal profoundly decreases the fitness of individual tumor cells. Resistance may be achieved rapidly if the EGFR-mut signal can be replaced (e.g., by a T790M mutation); but, in the absence of such a mutation, rebuilding the network will require extensive molecular rewiring resulting in a durable response [57]. In contrast, the smaller KRAS-mut network suggests response to targeted therapy will be less complete and durable than with EGFR-mut.

We hypothesize a requirement for more mutations to address a fixed number of environmental selection forces accounts for observations that EGFR-mut LUADs occur in younger patients than the other subtypes. That is, if a single mutation in one of the driver genes overcomes multiple barriers simultaneously, it will greatly accelerate the evolution of the malignant phenotype. However, in general, development of all lung cancers will be favored by an increased mutation rate which will allow them to generate the requisite mutations more quickly. This is consistent with increased risk of KRAS-mut, and NEK tumors associated with smoking while EGFR-mut cancers are frequently observed in non-smokers [58]. We note the mutational burden is increased by smoking [59] and a distinctive pattern of mutations is typically observed in smokers [20].

Our results have implications for the hypothesis that the “mutator phenotype” is essential for carcinogenesis. We find EGFR-mut cancers have a significantly lower mutational burden than the other subtypes and conserve 16 genes related to DNA repair compared to 1 and 3 in NEK and KRAS-mut subtypes, respectively. Extensive conservation of homeobox and RNA Polymerase II genes in the EGFR-mut cancers, suggests phenotypic plasticity is promoted through epigenetic modifications. In contrast, KRAS-mut and NEK tumors, which more commonly evolve in a mutagenic smoking environment, conserved fewer repair genes suggesting phenotypic heterogeneity is primarily driven by mutations.

While gene mutations are extensively investigated in cancers, the concept of conservation of normal genes under epigenetic control highlights the perhaps equally important role of conserved genes. Identifying genes under stabilizing selection can, thus provide novel insight into molecular pathways and cellular function critical for cancer cell survival and proliferation.

Finally, computer simulations (Supplementary Figure S5) have predicted targeting conserved genes may reduce the tumor population as effectively as targeting driver genes [1]. However, if a conserved gene is also necessary for survival of normal cells, toxicity could be limiting. Our findings that most conserved genes are highly specific to each lung cancer type suggest that they could be targeted without significant impact on normal tissue. These evolutionary dynamics, termed co-adaptation, have potential clinical significance. Both EGFR-mut and KRAS-mut cancers are now being treated with targeted therapy. While often initially effective, most cancer populations succeed in evolving resistance leading to tumor progression. Often, resistance emerges from proliferation of a rare population with a pre-existent resistance mutation (e.g., T790m). However, even when that specific mutation is treated, EGFR-mut lung cancers access alternative genetic pathways to overcome therapy. Simulations of this process (Supplementary Figure S5) show that conserved genes play a critical role in these evolutionary dynamics and predict that combination treatments targeting a driver gene and a driver-specific conserved gene may impose on the cancer a virtually unsolvable evolutionary conundrum that results in complete population loss (1). These findings, of course, are theoretical but may be used to guide future empirical studies.

5. Conclusions

The vast literature in cancer biology has identified extensive molecular changes in lung cancers. However, studies performed in vitro with cell lines are potentially limited by the ecological context of the experiments. That is, human cancer cells maintained in culture evolve to adapt to the environmental selection forces imposed by in vitro ecological conditions, which are vastly different from those in situ. As a result, cancer cells inevitably evolve to a “culture morph” so that its molecular properties are optimized for fitness in vitro. These eco-evolutionary dynamics generate uncertainty regarding the clinical relevance of pre-clinical experimental results.

Here, we apply Darwinian principles to the large publicly available data on molecular changes in clinical lung cancers. We use an inverse problem approach based on the hypothesis that observable molecular characteristics in multiple members of a tumor cohort represent common tumor cells’ evolutionary strategies for successful adaptation to intracellular and extracellular barriers encountered during carcinogenesis and tumor progression.

Initial computer simulations found the evolutionary arc of a tumor is sensitive to initial conditions, which include the molecular state of the initiating cell and the environmental properties of the local tissue in which somatic evolution takes place. By analyzing adenocarcinomas originating in the same tissue but with different initiating mutations, we separated these two dynamics. In general, our findings suggest each lung cancer population must overcome a relatively fixed number of intracellular and extracellular barriers to proliferation. Common mutational patterns among the 3 LUAD subtypes suggest they represent adaptations to these barriers. However, initiating mutations in genes with large interactome (e.g., EGFR, TP53, and KRAS) probably allow the cell to overcome multiple barriers simultaneously and are, therefore, favored. In the absence of a driver mutation, cancer cells need to accumulate far more mutations thus favoring mutagenic tissue environments.

On the other hand, conserved genes are unique to each cancer type and thus represent co-adaptations to the initiating mutations. In general, conserved genes represent the molecular wires or the downstream effectors of oncogenic signals and, thus, provide potentially valuable insights into critical molecular pathways and cellular functions. Computer simulation predict that disrupting tumor specific conserved genes can have a therapeutic benefit equal to or greater than targeted therapy for driver gene mutations.