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Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, New YorkDepartment of Pathology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
High-grade neuroendocrine tumors of the lung such as SCLC are recalcitrant cancers for which more effective systemic therapies are needed. Despite their histopathologic and molecular heterogeneity, they are generally treated as a single disease entity with similar chemotherapy regimens. Whereas marked clinical responses can be observed, they are short-lived. Inter- and intratumoral heterogeneity is considered a confounding factor in these unsatisfactory clinical outcomes, yet the origin of this heterogeneity and its impact on therapeutic responses is not well understood.
New genetically engineered mouse models are used to test the effects of PTEN loss on the development of lung tumors initiated by Rb1 and Trp53 tumor suppressor gene deletion.
Complete PTEN loss drives more rapid tumor development with a greater diversity of tumor histopathology ranging from adenocarcinoma to SCLC. PTEN loss also drives transcriptional heterogeneity as marked lineage plasticity is observed within histopathologic subtypes. Spatial profiling indicates transcriptional heterogeneity exists both within and among tumor foci with transcriptional patterns correlating with spatial position, implying that the growth environment influences gene expression.
These results identify PTEN loss as a clinically relevant genetic alteration driving the molecular and histopathologic heterogeneity of neuroendocrine lung tumors initiated by Rb1/Trp53 mutations.
SCLC has an aggressive disease course such that, at the time of diagnosis, most patients already present with metastatic disease. The 5-year survival rates are poor at less than 7%. The standard of care for treating SCLC remains chemotherapy, with the potential use of immunotherapy more recently considered. Whereas excellent clinical responses can be observed, therapeutic resistance develops rapidly resulting in a median survival duration of about 1 year. On the basis of these disappointing therapeutic outcomes, SCLC has been classified as a recalcitrant disease.
Yet a variety of additional cellular characteristics may be observed like large cells, pleiomorphic cells, or giant cells. Tumors typically grow as diffuse sheets but can also exhibit palisading, streams, ribbons, organoid nesting, and rosettes.
Histologic transformation from lung AD to an NE cancer resembling SCLC has also been reported, typically in association with acquired resistance to molecularly targeted therapies like EGFR tyrosine kinase inhibitors.
In addition to this histopathologic heterogeneity, transcriptional profiling has distinguished multiple molecular subtypes based on the expression of lineage specifying transcription factors ASCL1, NEUROD1, POU2F3, and YAP1.
although not all RB1/TP53-mutant lung tumors develop into SCLC. Other genetic alterations such as those in the PIK3A/PTEN pathway are also recurrent in SCLC. Yet, none of these genetic alterations clearly associate with molecular or histologic subtypes. Thus, it remains incompletely understood how molecular and histopathologic heterogeneity arises in SCLC. As SCLC is not often resected in the clinic because of its highly metastatic nature, genetically engineered mouse models have become important tools to address this knowledge gap.
including SPC-expressing AT2 alveolar cells. How the phenotype of different cells of cancer origin influences the histologic and molecular heterogeneity of lung cancers is not well understood. Here, we address these issues by performing detailed histopathologic analysis and gene expression profiling of tumors developing in genetically engineered mouse models of lung cancer initiated by defined genetic alterations in specified cells of cancer origin.
Material and Methods
Mice and Adenoviral Infection
Rb1flox, Trp53flox, and Ptenflox mouse alleles have been described previously.
Experimental mice were on a mixed genetic background (C57BL/6:129/Sv). Mice were monitored daily, euthanized when moribund, and necropsied to verify the diagnosis and collect tissue. Survival analysis by the Kaplan-Meier method was done with GraphPad Prism software (GraphPad Software, San Diego, CA). Ad-CMV-Cre and Ad-SPC-Cre were described previously,
All animal experiments comply with the Guide for the Care and Use of Laboratory Animals and have been approved by the Institutional Animal Care and Use Committee at Roswell Park.
Histologic and Immunostaining Analysis
Lung tissue was fixed in phosphate-buffered 4% paraformaldehyde, embedded in paraffin, and serially sectioned at 5-μm thickness. Sections were stained using hematoxylin and eosin (H&E) for histopathologic assessment (Liu, Zhang, Zheng, Zhu, and Bshara reviewing pathologists). Primary antibodies and antibody dilutions used for immunohistochemistry were: SYP (Thermofisher #PA5-16417; 1:600) (Thermo Fisher Scientific, Waltham, MA), TTF-1 (Invitrogen #MA5-13961; 1:600) (Thermo Fisher Scientific), Ki67 (Leica, Deer Park, IL, #NCL-Ki67p; 1:1000) (Leica Biosystems, Wetzlar, Germany), and SPC (Sigma-Aldrich, St. Louis, MO, #AB3786; 1:2000) (Sigma-Aldrich, Merck Group). Immunostaining was developed using diaminobenzidine (Dako, Santa Clara, CA, K3468) (Agilent Technologies, Santa Clara, CA) followed by hematoxylin counterstaining.
The diagnosis of cancer histologic subtypes is based on the WHO classification of tumors of the lung. SCLCs are malignant epithelial tumors expressing NE markers and composed of small cells, usually round, oval to spindle in shape with scant cytoplasm. The nucleoli are absent and inconspicuous with finely granular nuclear chromatin, frequently with a high mitotic rate. Tumor cells grow in a sheet-like or nest-like pattern, frequently with necrosis. LCNEC is a high-grade NSCLC expressing NE markers and sharing some NE histopathology, such as organoid nesting and rosette pattern. Cells are moderate to abundant cytoplasm with distinct borders. Some tumors have fine nuclear chromatin with nucleoli features analogous to SCLC. ADs usually grow in mixed patterns including lepidic, acinar, solid, papillary, and micropapillary. AD with NE differentiation (AD-NE) is defined as tumors with AD histopathology but NE marker expression, indicating NE differentiation. Atypical papillary bronchiolar proliferation is proliferation of the bronchiolar epithelium. The most prominent feature is papillary proliferation with a central fibrovascular core and mildly atypical columnar epithelial cells.
A central location for tumors is defined as those that grow in or around major central airways. A peripheral location is defined as tumors that grow in or around terminal airways in the peripheral lung. A diffuse pattern is defined as tumors covering most of the lung. Tumor burden was determined on the basis of the percentage of lung area occupied by tumors as measured using ImageJ software (National Institutes of Health and the Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI). Histologic quantification was performed by quantifying the frequency of cells that were positive for specified antigens and staining intensity scoring from 0 (negative) to 3 (very dark staining).
The sequencing libraries were prepared with the RNA HyperPrep Kit with RiboErase (HMR) kit (Roche Sequencing Solutions), from 500-ng total RNA, following the manufacturer’s instructions. The first step depletes ribosomal RNA from the total RNA. After ribosomal depletion, the remaining RNA is DNase digested to remove genomic DNA contamination. Samples were then purified, fragmented, and primed for complementary DNA (cDNA) synthesis. Fragmented RNA was then reverse-transcribed into first-strand cDNA using random primers. The next step is the removal of the RNA template and synthesis of a replacement strand, incorporating dUTP in place of dTTP to generate double-stranded cDNA (ds cDNA). Pure Beads (Kapa Biosystems, Corston, Bath, United Kingdom) were used to separate the ds cDNA from the second strand reaction mix resulting in blunt-ended cDNA. A single ‘A’ nucleotide is then added to the 3′ ends of the blunt fragments. Multiple indexing adapters, containing a single ‘T’ nucleotide on the 3′ end of the adapter, were ligated to the ends of the ds cDNA, preparing them for hybridization onto a flow cell. Adapter ligated libraries were amplified by PCR, purified using Pure Beads, and validated for appropriate size on a 4200 TapeStation D1000 Screentape (Agilent Technologies, Inc.). The DNA libraries were quantitated using Kapa Biosystems quantitative PCR kit and were pooled together in an equimolar fashion. Each pool was denatured and diluted to 350 pM with 1% PhiX control library (Illumina, San Diego, CA) added. The resulting pool was then loaded into a 200-cycle NovaSeq Reagent cartridge (Illumina) for a 100-cycle paired-end sequencing using a NovaSeq6000 (Illumina) following the manufacturer’s recommended protocol.
RNA Sequencing Analysis
Paired-end raw sequencing reads passing quality filters from Illumina Real-Time Analysis were first preprocessed using FastQC (version 0.10.1) (Babraham Bioinformatics, The Babraham Institute, Babraham, Cambridge, United Kingdom) for sequencing base quality control.
The top 300 interquartile range (IQR)–regularized log2 transformed expression genes were used to run Hierarchical Clustering Analysis to determine prominent clustering profiles. The IQR statistic was used to robustly detect highly variable genes. The data were fitted with the hclust function from the stats native R (version 4.1.1) package, using the “complete” clustering method paired with Euclidean distance. Clustering groups were selected by cutting the four main branches resulting from the hierarchical clustering tree. The clusters were manually curated and given the following labels on the basis of their gene profiles: “cl1-Adeno” (1), “cl2-LungClub” (3), “cl3-Dediff” (5), and “other” (4). Only the first three groups were considered for downstream analyses. Pairwise group differential expression analyses were carried out, using DESeq2 (Bioconductor), to determine overexpressed sets of genes that characterize each clustering group. Gene set enrichment analysis  was then used to further evaluate the association of the cluster groups with canonical biological pathways using C2cp database from the molecular signatures database (version 7.4).
Spatial profiling was performed using 10x Visium Spatial for formalin-fixed paraffin-embedded (FFPE) Gene Expression Kit (Spatial 3’ v1) (10X Genomics Inc., Pleasanton, CA). Briefly, the tissue-embedded paraffin blocks were sectioned and trimmed to fit within the four capture areas on the Visium Spatial slides. Deparaffinization and H&E staining was performed, followed by imaging of each tissue sample. The RNAs within the tissue were then hybridized to mouse whole transcriptome probe panel and hybridized probes were captured on the Visium slides. Captured probe products were extended with the addition of unique molecular identifiers, spatial barcode, and partial read 1, thus, synthesizing the cDNA to be used for gene expression library construction. The FFPE gene expression libraries for each sample were produced with enzymatic fragmentation, end-repair, a-tailing, adapter ligation, and PCR to add Illumina-compatible sequencing adapters. The resulting libraries were evaluated on D1000 screentape using a TapeStation 4200 (Agilent Technologies) and quantitated using Kapa Biosystems quantitative PCR quantitation kit for Illumina. They were then pooled, denatured, and diluted to 300 pM with 1% PhiX control library (Illumina) added. The resulting pool was then loaded into the appropriate NovaSeq Reagent cartridge (Illumina) and sequenced on a NovaSeq6000 (Illumina) following the manufacturer’s recommended protocol. Once sequencing was complete, tissue images were taken after H&E staining was used to align the FFPE gene expression from the spatial barcodes unique to each location in the capture area during data analysis using Space Ranger v 1.3.1 software (10× Genomics).
The raw sequencing data, mapping results (binary alignment and map files), and quantification matrices were generated using Cellranger software (10× Genomics) with mouse mm 10 genome and GENCODE annotation database. Seurat single-cell data analysis R package was used for spatial RNA-sequencing (RNA-seq) data analysis.
The gene counts matrices were normalized with the SCTransform method and dimension reductions—including principal component analysis, uniform manifold approximation and projection, and t-distributed stochastic neighbor embedding—were carried out for the highly variable genes. Data clustering is identified using the shared nearest neighbor–based clustering on the first 23 principal components. The NE score was calculated using the AddModuleScore method with a list of NE marker genes.
PTEN Loss Accelerates Lung Cancer Progression Initiated by Rb1/Trp53 Loss
We generated mice with the Rb1flox/flox:Trp53flox/flox:Ptenflox/+ or Rb1flox/flox:Trp53flox/flox:Ptenflox/flox genotypes to assess the effects of PTEN loss on lung cancer progression initiated by Rb1/Trp53 double knockout (DKO) (Fig. 1A). Tumorigenesis was initiated by intratracheal injection of Ad-CMV-Cre or Ad-SPC-Cre. Ad-CMV-Cre expresses Cre in a broad range of lung cell types whereas Ad-SPC-Cre restricts Cre-mediated recombination to SPC-expressing AT2 cells.
Homozygous deletion of PTEN in DKO mice reduced mouse survival, relative to mice retaining one wild-type PTEN allele when tumorigenesis was initiated by either Ad-CMV-Cre or Ad-SPC-Cre (Fig. 1B). For the Ad-CMV-Cre–initiated mice, the median survival was significantly different between DKO:Ptenflox/flox (119 d postviral administration) and DKO:Ptenflox/+ mice (203 d) (log-rank p < 0.01), consistent with published reports.
Survival was longer in all Ad-SPC-Cre–initiated cohorts, but the median survival for DKO:Ptenflox/flox mice (259 d) was still significantly shorter than for DKO:Ptenflox/+ mice (364 d) (log-rank p < 0.01). Cre-mediated deletion of floxed Rb1, Trp53, and PTEN alleles was confirmed by PCR (Fig. 1C). Thus, complete PTEN loss accelerates the development of lethal lung cancers compared with the loss of one PTEN allele, both when initiated in a broad range of lung cell types or when initiated in SPC-expressing AT2 cells.
Primary tumor burden at the disease end point was measured for each cohort on the basis of the fractional area of lung tissue occupied by tumors (Fig. 1D). Tumor burden was different in the four cohorts (one-way analysis of variance [ANOVA] p < 0.01) with greater average tumor burden in Ad-SPC-Cre–initiated mice. Tumor burden at the end stage was significantly greater for Ad-SPC-Cre–initiated DKO:Ptenflox/flox mice than for Ad-CMV-Cre–initiated DKO:Ptenflox/flox mice (t test p < 0.01), for example. Given the lack of correlation between primary tumor burden and survival, we assessed mice for evidence of metastasis. All cohorts had evidence of metastatic dissemination from the lung to the liver (Fig. 1E). However, the fraction of mice exhibiting detectable liver metastasis was higher for Ad-SPC-Cre–initiated cohorts than for Ad-CMV-Cre–initiated cohorts. The reduced survival of Ad-CMV-Cre–initiated mice, therefore, was not explained by increased primary or metastatic tumor burden. Instead, primary and metastatic tumor burden correlated with survival time suggesting extended survival allowed more time for primary tumor growth and metastatic dissemination.
PTEN Loss Diversifies Lung Cancer Histopathology
We performed detailed histopathologic characterization of end-stage tumors from mice in each of the cohorts to determine whether differences in tumor phenotype might correlate with mouse survival. The criteria used for classification were on the basis of standard definitions according to the WHO and published literature.
Typically, mice contained multiple tumors distributed across all lobes of the lung, and multiple tumors within a given mouse often exhibited different histopathology. The two major tumor types observed in Ad-CMV-Cre–initiated DKO:Ptenflox/flox mice, on the basis of tumor area, were SCLC and AD-NE. AD-NE are defined as tumors with AD histopathology but that express NE markers like synaptophysin. LCNEC and AD were also observed (Fig. 2A and B). Lung cancers in Ad-CMV-Cre–initiated DKO:Ptenflox/+ mice were primarily SCLC and largely devoid of AD. Complete PTEN loss, therefore, expands the histopathology of resulting lung cancers initiated by Ad-CMV-Cre. The major lung cancer types observed in Ad-SPC-Cre–initiated DKO:Ptenflox/flox and DKO:Ptenflox/+ mice were SCLC and AD, with a substantial fraction of LCNEC (Fig. 2A). Both AD tumor burden and incidence were higher in Ad-SPC-Cre–initiated cohorts compared with Ad-CMV-Cre–initiated cohorts (Fig. 2A), indicating that SPC-expressing AT2 cells were more susceptible to develop AD and less likely to exhibit early development of lethal NE tumors.
We immunostained tissue sections for SYP and TTF-1 to confirm diagnoses (Fig. 2C). Nearly 90% of all tumor cells stained positive for TTF-1 in each of the cohorts, distinguishing these tumors from other possible lung cancer types like squamous carcinoma. SYP-positive tumors developed in all mice examined, whereas SYP-negative AD developed in about two-thirds of mice, particularly in the Ad-SPC-Cre–initiated cohorts (Fig. 2D). Most NE-positive tumors in the Ad-CMV-Cre–initiated cohorts were located centrally near the major airways, consistent with the anatomical location of most murine pulmonary NE cells.
Tumors arising in the Ad-SPC-Cre–initiated cohorts were more evenly distributed between central and peripheral locations, consistent with the anatomical location of AT2 cells. We hypothesize that early developing NE tumors located near the major airways explain the shorter lifespan of Ad-CMV-Cre cohorts.
Clinically, major histologic AD subtypes correlate with prognosis.
A micropapillary pattern is predictive of a poor prognosis in lung adenocarcinoma, and reduced surfactant apoprotein A expression in the micropapillary pattern is an excellent indicator of a poor prognosis.
Lung cancer genetically engineered mouse models initiated by Rb1/Trp53 deletion have not been systematically analyzed for analogous AD subtypes, so we addressed this here. AD developing in the four cohorts exhibits a range of different subtypes (Fig. 2E). Interestingly, the distribution of AD subtypes varies depending on genotype and the cell of origin (Fig. 2F). Nearly all AD developing in Ad-CMV-Cre–initiated mice are lepidic, with the rarer occurrence of a solid growth pattern (2 of 17 mice). Atypical papillary bronchiolar proliferation, which has been suggested to be a premalignant lesion,
is also observed in some of these mice. Lepidic and acinar AD are the predominant growth patterns in Ad-SPC-Cre–initiated DKO:Ptenflox/flox mice. Ad-SPC-Cre–initiated DKO:Ptenflox/+ mice exhibit the broadest range of AD subtypes, including the more aggressive solid and micropapillary subtypes. Acinar, solid, papillary, and micropapillary AD is observed in a single mouse surviving 67 weeks after virus delivery. As the greatest range of AD subtypes are observed in the longest-lived Ad-SPC-Cre–initiated DKO:Ptenflox/+ mice, the detection of these subtypes may be precluded in other cohorts by earlier developing lethal NE tumors.
We have examined tumor tissues collected 6 to 10 weeks earlier than the median survival time for each cohort to assess cancer histopathologic diversity earlier during cancer progression. Tumor burden is similar between cohorts at these earlier times of collection, although there is a wide range of tumor burden in different mice within the same cohort (Fig. 3A). We observed clear differences in the distribution of tumor type fractional area between cohorts at the early time points and between the early and late time points for Ad-SPC-Cre–initiated mice (Fig. 3B). In general, SCLC has reduced incidence (Fig. 3C) and makes up a smaller fraction of tumor area at the early time points. In contrast, AD makes up a greater fraction of tumor area at these early time points. For example, AD contributes 80% and SCLC 17% to the total tumor area at early time points in Ad-SPC-Cre–initiated DKO:Ptenflox/flox mice, but 21% and 51% 6 to 10 weeks later. Tumor sections from Ad-SPC-Cre–initiated mice developing both AD and NE tumors were immunostained for the proliferation marker KI67 and the fractional Ki67 positive tumor area compared. The average Ki67-positive tumor area was high for both (Fig. 3D), indicative of the high proliferative index expected of tumors cancers lacking Rb1/Trp53. Yet, KI67 immunostaining was significantly higher for NE tumors than for AD (Fig. 3E) (t test p = 0.005). These data indicate that high-grade NE tumors likely develop later than AD tumors, but they proliferate and expand faster.
PTEN Loss Drives Lung Cancer Molecular Heterogeneity
SYP is a NE marker used to diagnose SCLC and other NE tumors. SYP-positive tumor foci were detected in a large fraction of mice from all cohorts, although SYP-positive fractional area was reduced in the Ad-SPC-Cre–initiated cohorts (Fig. 4A and B). To investigate this heterogeneity further, we quantitated SYP immunostaining on the basis of intensity (Fig. 4C). Most Ad-SPC-Cre–initiated tumors exhibited weaker SYP immunostaining, consistent with the greater prevalence of AD compared with Ad-CMV-Cre–initiated tumors. Ad-CMV-Cre–initiated DKO:Ptenflox/+ mice had the highest fraction of SYP-positive tumor area and the highest SYP immunostaining intensity, again matching the tumor subtype prevalence as nearly 90% of tumors arising in this cohort were SCLC. Although we observed heterogeneous SYP expression in primary tumors, all liver metastases developing in mice from the four cohorts exhibited uniformly strong SYP immunostaining and histopathology consistent with SCLC (Fig. 4D). Of the primary tumor subtypes identified, therefore, SCLC has the greatest metastatic potential.
RNA-seq analysis has been performed on representative lung tissue from mice in each of the four cohorts to assess transcriptional heterogeneity between mice and cohorts. Principal component analysis and hierarchical clustering indicate that gene expression does not correlate well with genotype or adenovirus treatment, except for the Ad-CMV-Cre–initiated DKO:Ptenflox/flox cohort whose samples cluster closely (Fig. 5A and B). Gene expression varied considerably between samples within the remaining cohorts, with Ad-SPC-Cre–initiated tumors covering a wider area of principal component space (Fig. 5C). This is consistent with findings in human lung cancers in which gene expression does not correlate well with underlying genetic alterations.
The closely clustering samples from the Ad-CMV-Cre–initiated DKO:Ptenflox/flox cohort (3F12, 3F21, 3F30) reveal reduced NE gene expression and increased immunomodulatory and defense response gene expression relative to other clusters (Fig. 5D), functions consistent with normal pulmonary club cell function.
Indeed, these samples uniquely express club cell marker genes (Fig. 5E). Another looser cluster of four samples with predominant SCLC histopathology (3F10, T1303, T26, T1822) exhibit elevated expression of lineage specifying transcription factors expressed in retinal lineages but not normally in the lung (Lhx1, Dmbx1) (Fig. 5F). This group of samples also expressed higher levels of Mycl and Nfib. Nfib is amplified recurrently in mouse SCLC tumors initiated by Rb1/Trp53 deletion, can drive SCLC development, and has been associated with SCLC metastasis.
Even though two of these samples were initiated with Ad-SPC-Cre, they all exhibit relatively low expression levels of AT2 cell marker genes (Sftpc, Lamp3). SCLC in these four samples is, thus, molecularly distinct from other samples with predominant SCLC histopathology (e.g., T1599, T31). All SCLC developing in these mice seem to be of the Ascl1 high subtype given Pou2f3 and NeuroD1 expression is low or undetectable (Supplementary Table 1). Yap1 expression is highest in the club cell–like cluster of samples. Thus, there is considerable transcriptional heterogeneity within the Ascl1 high NE tumor subtype.
End-stage lung tissues analyzed above are likely composed of multiple tumor foci; thus, the bulk RNA-seq data reflect potential admixtures of tumor subtype heterogeneity prevalent in individual tissues analyzed. To account for this, we have performed spatial transcriptomic profiling of tissue sections from the same mice. The 12 samples analyzed in aggregate generated 39 distinguishable transcriptional clusters (Fig. 6A). Consistent with bulk RNA-seq analysis, most clusters are private to a particular sample indicating substantial gene expression heterogeneity between mice even within a cohort (Fig. 6B). The exception is the Ad-CMV-Cre–initiated DKO:Ptenflox/flox cohort whose samples cluster more closely together. An NE signature score was calculated for each spot on the spatial transcriptomic profiles, and this score was mapped to the gene expression clusters (Fig. 6C). The NE score varied considerably between and within samples; the predominant AD sample (T1821) exhibits the lowest NE score, as expected. The club cell–like clusters exhibit relatively low NE scores consistent with bulk RNA-seq data. Samples with predominant SCLC histopathology vary in NE score considerably, with the four Nfib+ samples (3F10, T1303, T1822, T26) expressing lower NE scores than other samples with predominant SCLC histopathology (T1303, T1599). Because of the transcriptional heterogeneity observed, more experiments and analyses will be required to test whether histopathologic subtypes share a common gene expression program.
Mapping the 39 transcriptional clusters to the tissue sections indicates these transcriptional clusters are typically spatially distinct with some limited spatial intermixing (Supplementary Fig. 1). The Ad-CMV-Cre–initiated DKO:Ptenflox/flox cohort contains the tumor cluster with the most elevated club cell marker gene expression (cluster 14) (Supplementary Table 2). This cluster is shared across the different samples in this cohort. Furthermore, the histopathology corresponding to this cluster is predominantly LCNEC across all samples in this cohort, demonstrating reproducibility and sugggesting a potential association between cancers originating within club cells and LCNEC histopathology. Including only tumor area, Ad-SPC-Cre–initiated DKO:Ptenflox/flox samples expressed more distinct transcriptional clusters (9) than Ad-SPC-Cre–initiated DKO:Ptenflox/+ samples (5).
When samples are analyzed individually, more transcriptional clusters are detectable per sample. For example, 15 distinct transcriptional clusters are distinguished in sample T1297 that exhibit predominant LCNEC histopathology (Fig. 6D and Supplementary Fig. 2). Mapping these more highly resolved transcriptional clusters spatially also indicates limited intermixing (Fig. 6E). Tumor foci alone encompass at least seven of the 15 transcriptional clusters identified. The NE gene expression score varies both between and within spatially separated tumor foci. There is evidence that cells near the periphery of tumor foci have distinct gene expression profiles compared with cells near the center of tumor foci (cluster 0), perhaps reflecting the effect of distinct cell microenvironments. Some transcriptional clusters are unique to an area within individual tumor foci (clusters 11, 14). These two clusters retain gene expression characteristic of AT2 cells as they are initiated by Ad-SPC-Cre, they have relatively low NE gene expression score, and they significantly differentially expressed genes (adjusted p < 0.05) normally expressed in cell lineages outside of lung epithelium. Examples of alternative lineage gene expression in these clusters include mesenchymal (Wnt11, Serpine1, Cldn11), squamous (Calml3, Ivl), retinal (Vgf), glandular (Upk3a), and intestinal (Nkx2-2) (Supplementary Table 3). Overall the gene expression analysis suggests complete PTEN loss expands the lineage plasticity of resulting lung tumors.
Detailed histopathologic characterization and spatial transcriptomic profiling of new mouse models have been conducted to test how PTEN loss influences the phenotype of lung cancers initiated by Rb1/Trp53 deletion. PTEN loss of function is a clinically relevant genetic alteration as it is recurrent in human SCLC (∼6% of cases [cBioPortal]). Furthermore, PTEN loss of function alterations arises spontaneously and recurrently in mouse models of SCLC initiated by Rb1/Trp53 deletion,
suggesting that PTEN loss of function is selected during tumor evolution. We confirm this hypothesis here as complete PTEN deletion accelerates lung tumorigenesis initiated by Rb1/Trp53 loss, thus, shortening mouse survival compared with mice lacking only one PTEN allele. Results are consistent with previously published work in which Rb1/Trp53/PTEN were deleted in CGRP expressing pulmonary NE cells or using Ad-CMV-Cre.
Our results extend this conclusion to SPC-expressing AT2 cells, the presumed cell of origin for most lung ADs. The median survival of Ad-SPC-Cre–initiated DKO:Ptenflox/flox (259 d) and DKO:Ptenflox/+ (364 d) mice are markedly shorter than that reported previously for Ad-SPC-Cre–initiated mice lacking Rb1/Trp53 alone (462 d).
Overall, lung cancer penetrance is also higher (100% versus 73%). Although not tested directly here, these findings suggest loss of one PTEN allele may also accelerate lung tumorigenesis and shorten mouse survival.
Earlier work did not characterize the histopathology of lung tumors developing on PTEN/Rb1/Trp53 deletion nor were these alterations restricted to AT2 cells. Through detailed histopathologic characterization of lung tumors initiated with these genetic alterations in AT2 cells (Ad-SPC-Cre), we can conclude that deleting PTEN/Rb1/Trp53 expands the phenotypic heterogeneity of resulting lung cancers. For example, Rb1/Trp53 deletion in AT2 cells has been reported to generate SCLC with histopathologic and molecular features analogous to SCLC arising from pulmonary NE cells.
In contrast, we observe that PTEN/Rb1/Trp53 deletion in AT2 cells generates a substantial fraction of LCNEC, AD, and AD-NE. Some mice develop LCNEC predominantly, making these mice particularly useful for studying this disease state given the paucity of available LCNEC mouse models. Histopathologic diversity within individual mice is greater on complete loss of PTEN/Rb1/Trp53 in AT2 cells compared with AT2 cells retaining one wild-type PTEN allele. A markedly higher incidence of AD is observed in Ad-SPC-Cre–initiated cohorts studied here compared with previously published studies deleting Rb1/Trp53 alone (80% versus 3%), suggesting PTEN deficiency increased the likelihood initiated AT2 cells develop AD. The diversity of AD pathologic subtypes arising varies depending on the cell of origin and PTEN allele dosage. The most diverse AD histopathology is observed in the longest-lived mice, likely because AD is slower growing. There is a substantial mouse-to-mouse variation both between and within mouse cohorts with respect to tumor burden and histopathology. Overall, these observations are consistent with the hypothesis that PTEN loss increases the susceptibility of Rb1/Trp53-deficient AT2 cells to neoplastic transformation and drives the phenotypic heterogeneity of resulting tumors.
Spatial transcriptomic profiling exhibits significant molecular heterogeneity in tumors developing in mice among different cohorts, among mice within the same cohort, among spatially distinct tumor foci within individual mice, and within individual tumor foci. Lineage plasticity is elevated in these tumors with expressions of genes normally restricted to lineages outside of the lung. Given this transcriptional plasticity, the tumor cell growth environment likely influences phenotype. Spatial profiling provides evidence in support of this hypothesis as the transcriptional patterns cluster on the basis of the tumor cell’s position with limited spatial intermixing. For example, cells at the periphery of a tumor foci can have a transcriptional pattern distinct from cells at the center of the foci. NE tumors arising in Ad-CMV-Cre–initiated DKO:Ptenflox/flox mice are an exception because their gene expression is similar between mice. These tumors uniquely express genes normally restricted to the club cell lineage. Whereas lineage tracing approaches are required to rigorously test this, the finding suggests club cells are susceptible to infection by Ad-CMV-Cre and can serve as the cell of origin for these lung tumors. Tumors form rarely when Rb1/Trp53 alone are deleted in club cells specifically,
implying that complete loss of PTEN can increase the susceptibility of club cells to neoplastic transformation. Alternatively, PTEN loss may drive lung cancer lineage plasticity sufficiently to allow reprogramming to a club cell–like transcriptional phenotype, although this is not observed when tumors are initiated with Ad-SPC-Cre.
Genetically engineered mouse models have been instrumental in deciphering the contributions of genetic alterations and the cell of origin to the development of lung cancers in general
Results here extend this body of literature by demonstrating that PTEN deficiency, when combined with Rb1/Trp53 alteration, can drive both more efficient neoplastic transformation of AT2 cells and elevated cancer lineage plasticity. We note that similar genetic interactions between Rb1, Trp53, and Pten in driving neoplastic transformation and lineage plasticity have also been observed in prostate cancer.
The diversity of tumor phenotypes arising in these new mouse models will be potentially useful for future studies aimed at understanding how genetic mutations and the cell or origin collaborate to influence lung cancer heterogeneity and assessing the relative sensitivity of different lung cancer subtypes to cancer therapy.
CRediT Authorship Contribution Statement
Letian Zhang: Conceptualization, Data collection, Data analysis, Methodology, Writing.
Congrong Liu: Data collection, Pathological analysis, Methodology, Validation.
Bo Zhang: Data collection, Pathological analysis, Methodology, Validation.
Jie Zheng: Data collection, Pathological analysis, Methodology, Validation.
Prashant Singh: Data collection, Data analysis, Methodology.
Wiam Bshara: Data collection, Pathological analysis, Supervision, Validation.
Jianmin Wang: Data curation, Bioinformatic analysis, Supervision.
Eduardo Cortes Gomez: Data curation, Bioinformatic analysis.
David W. Goodrich: Conceptualization, Data analysis, Validation, Funding acquisition, Supervision, Writing.
This research was supported by grants to Dr. Goodrich (National Cancer Institute [NCI] R01 CA234162, NCI R01 CA207757, and the Roswell Park Alliance Foundation) and Dr. Zhu (National Natural Science Foundation of People's Republic of China: 81300045). This work was also supported by the Roswell Park Comprehensive Cancer Center and the NCI grant P30 CA016056 supporting the use of the Genomics, Bioinformatics, Experimental Tumor Models, and Comparative Oncology shared resources.
A micropapillary pattern is predictive of a poor prognosis in lung adenocarcinoma, and reduced surfactant apoprotein A expression in the micropapillary pattern is an excellent indicator of a poor prognosis.