Source, co-occurrence, and prognostic value of PTEN mutations or loss in colorectal cancer

Serebriiskii, Ilya G.; Pavlov, Valerii A.; Andrianov, Grigorii V.; Litwin, Samuel; Basickes, Stanley; Newberg, Justin Y.; Frampton, Garrett M.; Meyer, Joshua E.; Golemis, Erica A.

doi:10.1038/s41525-023-00384-7

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Published: 24 November 2023

Source, co-occurrence, and prognostic value of PTEN mutations or loss in colorectal cancer

npj Genomic Medicine volume 8, Article number: 40 (2023) Cite this article

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Abstract

Somatic PTEN mutations are common and have driver function in some cancer types. However, in colorectal cancers (CRCs), somatic PTEN-inactivating mutations occur at a low frequency (~8–9%), and whether these mutations are actively selected and promote tumor aggressiveness has been controversial. Analysis of genomic data from ~53,000 CRCs indicates that hotspot mutation patterns in PTEN partially reflect DNA-dependent selection pressures, but also suggests a strong selection pressure based on protein function. In microsatellite stable (MSS) tumors, PTEN alterations co-occur with mutations activating BRAF or PI3K, or with TP53 deletions, but not in CRC with microsatellite instability (MSI). Unexpectedly, PTEN deletions are associated with poor survival in MSS CRC, whereas PTEN mutations are associated with improved survival in MSI CRC. These and other data suggest use of PTEN as a prognostic marker is valid in CRC, but such use must consider driver mutation landscape, tumor subtype, and category of PTEN alteration.

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Introduction

Colorectal cancer (CRC) is currently the third most common cause of cancer death in the United States, resulting in 53,000 deaths annually¹, and is the second most common globally. Rates of some forms of colorectal cancers have been increasing for the past several decades^2,3, including particularly in younger patients, with lifestyle factors one contributing factor⁴. The most common oncogenic drivers of CRC include loss of function (LoF) (by mutation or deletion) of the tumor suppressors APC and TP53, and activating mutations in KRAS or BRAF. Less common but occurring in ~8–9% of CRC are non-synonymous mutations in the tumor suppressor PTEN⁵, a lipid phosphatase that is a negative regulator of PI3K/AKT/mTOR signaling. The tumor suppressor role of PTEN is well-established in a number of cancers in which somatic PTEN mutations are very common, such as glioblastomas⁶. Cowden’s Syndrome and other syndromes associated with germline mutations impairing PTEN function are also associated with a high risk for several forms of cancer⁷. However, because of the lower mutational frequency, the prognostic and predictive role of PTEN mutations in affecting the biology and prognosis of CRC remains controversial. Particularly given the availability of drugs to target tumor-promoting signaling pathways negatively regulated by PTEN, an accurate understanding of how PTEN mutations arise, contribute to tumor presentation, and influence outcomes is of value.

In previous work, we analyzed mutational data from a large cohort of 34,129 CRC patients that allowed description of mutational patterns in PTEN and other genes associated with microsatellite stable (MSS) CRC, or CRC characterized by microsatellite instability (MSI)^5,8,9. For PTEN, this revealed distinct patterns of mutational hotspots associated with MSS versus MSI tumors, with the MSS tumors more likely to be characterized by mutations that significantly impair the lipid phosphatase activity (LPA) of PTEN by destabilizing the protein and/or reducing protein enzymatic function, and correlation of specific mutational patterns with age of diagnosis with CRC⁵.

In this study, we have extended analysis to incorporate results from an additional 18,679 CRC tumor specimens, with many cases linked to data about prognosis. Initial analysis of the contribution of mutational signatures common in CRC to observed pattern of PTEN mutations indicated that although underlying mutational processes strongly influence the spectrum of observed PTEN mutations, other factors contribute to the selection process. One such factor is selection for impairment of PTEN function, as there is a strong bias for mutations causing PTEN destabilization or loss of lipid phosphatase activity. However, surprisingly, an analysis of the allele frequency of damaging versus non-damaging PTEN mutations showed both occurred at a similar frequency, arguing for additional selection exclusive of gene signature or impact on protein stability or lipid phosphatase activity. The correlation of PTEN mutations with specific driver mutations was observed, but varied with CRC subtype; for example, In MSS tumors, PTEN mutations had a marked pattern of co-occurrence with BRAF and TP53 mutations that was not observed in MSI tumors. Intriguingly, the presence of PTEN alterations was prognostic, but in a nuanced manner, with PTEN deletions suggesting negative prognosis in MSS tumors, but PTEN mutations indicating positive prognosis in MSI tumors. These data suggest significant differences in the regulation and role of PTEN in MSS versus MSI CRC, and generally support a nuanced use of PTEN alterations as a prognostic factor.

Results

Profile of PTEN mutational hotspots

We used an extended cohort combining colorectal tumor specimens from FMI, cBioportal, AACR-GENIE, and other sources of publicly available data (PAD) (Fig. 1a, Supplementary Fig. S1a, b), to augment and contrast with specimens available from FMI⁵. In the PAD dataset, 86.9% of tumors for which MS status was known or imputed were MSS/tumor mutation burden (TMB) low (designated MT-L), 13.0% MSI/TMB high (designated MT-H), and 0.1% MSS with a high TMB (MSS-htmb, often associated with POLE mutations) versus 94.6% MT-L, 4.7% MT-H, and 0.7% MSS-htmb in the FMI dataset (Fig. 1b; see discussion of rationale for use of MT-L, MT-H, and MSS-htmb terminology in methods and in⁵). The frequencies of PTEN alterations we observed in the combined PAD and FMI datasets are MT-L, 3.9%; MT-H, 18.1%; MSS-htmb, 44.0%.

**Fig. 1: Description of the datasets used in the study, and identification of *PTEN* alterations and alteration hotsponts.**

Analysis of the pattern of recurrently mutated amino acids in PTEN (e.g. hotspots) indicated similar profiles in the FMI and PAD datasets (Fig. 1c, Supplementary Table S2). To determine if specific sites of mutation are clustered on the 3D folded structure of the protein, we modeled PTEN with Alphafold2 (AF2) and ESMfold (Supplementary Fig. S1c), which allowed us to extend the coverage beyond that present in the experimental models. Using the 5 best models (Supplementary Fig. S1d) we identified statistically significant 3D hotspots (Supplementary Table S3), defined as overabundance of mutations occurring in a group of amino acids no more than 5\(\mathring{\rm A}\) apart in the PTEN monomer. This identified two new 3D hotspots partially encompassing the C2 domain of PTEN (Fig. 1d), in addition to 3D hotspots residing within the phosphatase domain. In addition, since PTEN is known to function as a multimer, we investigated whether mutations disrupt the PTEN dimeric interface. Interestingly, the interface between the PTEN molecules represents a “coldspot”, with mutations occurring there at ~3.8 times lower frequencies than elsewhere, in both MSS and MSI tumors (p-value = 0.003, Fig. 1e, see Supplementary Table S4 for specific affected residues).

Overall selection for non-synonymous PTEN mutations

For most oncogenes and tumor suppressors with cancer driver activity, function is context dependent, with few universally important across a range of tissues and organs^10,11. The driver status of PTEN mutations in CRC is ambiguous, with one study finding mutational inactivation of PTEN under selection in many tumor types, but not in CRC¹². One approach to assess functional selection pressures in cancer genomes is to calculate the ratio of non-synonymous to synonymous mutations (dN/dS) in a tumor type-specific manner. Using this approach, PTEN mutations were identified as tumor drivers in 16/29 tumor types, including CRC¹¹; however, this conclusion was drawn from the analysis of only 226 colorectal samples based on a complex statistical model. Using the extensive dataset in this study, we directly calculated dN/dS for PTEN benchmarked to the oncogenes KRAS and PIK3CA, and the tumor suppressors APC and TP53, considering non-truncating and truncating mutations separately (Fig. 2a). For KRAS and PI3K, there is a strong selection for non-truncating mutations and a negative selection against truncating mutations, reflecting the known requirement for specific missense mutations to activate these oncogenes. There is a very strong selection for APC truncating mutations, although non-truncating mutations are also positively selected. For both PTEN and TP53 there is similarly a very strong selection for both truncating and missense mutations; although the dN/dS is smaller for PTEN (suggesting a slightly higher burden of passenger mutations), nevertheless about 94% of non-truncating and 95% of truncating PTEN mutations are expected to be genuine driver mutations. To gain additional insight, we analyzed additional features of PTEN mutational landscape, including the hotspot profile, allele frequencies, and co-occurrence profiles, and impact on survival.

**Fig. 2: Relationship of selection pressures and *PTEN* mutation patterns.**

Nucleotide substitution profile in PTEN in CRC: mutational signatures versus functional selection

To investigate the underlying selection factors for PTEN hotspots, we first cross-validated the changes observed in the PAD and FMI datasets, comparing the specific nucleotide substitutions observed in PTEN, versus cumulative substitutions observed in all genes analyzed in CRC specimens in FMI or public data (Fig. 2b). This showed overall congruence between the two PTEN datasets. The observed differences, while statistically significant due to the very high sample count, likely reflect the distinct percentage of MT-L, MT-H, and MSS-htmb in the FMI versus PAD datasets, as these CRC subtypes have highly divergent nucleotide substitution profiles (Fig. 2c). We next used the frequencies of each possible base substitution in each of 96 different trinucleotide contexts (ref. ¹³, Supplementary Table S5) to predict the nucleotide substitution spectrum and frequency across the PTEN coding sequence in MT-L and MT-H tumors. The transitions/transversions profile actually observed in PTEN MT-L cohort versus predicted based on the nucleotide content of the gene was significantly different (Fig. 2d, Supplementary Table S6), suggesting the presence of additional mutability variables. We also analyzed in detail the changes from the most abundant classes of PTEN mutations produced by mutational signatures, common in CRC¹⁴, (the SBS1 and ID1/ID2/ID5/ID7 signatures (collectively designated hereafter as IDT, for total); Supplementary Fig. S2a–c). This indicated that deletions in polynucleotide runs are much more frequent than insertions in MT-H cohort, as is typical for cells with deficiency in mismatch repair (MMR) proteins¹⁵, while the opposite was true for the MT-L subset.

Focusing on non-synonymous single nucleotide substitutions in PTEN for the combined cohort (excluding MSS-htmb samples, comprising ~0.7% of the total, as too small a cohort to yield significant conclusions), we compared the observed frequency of mutations, versus the predicted frequency based on the occurrence of mutational signatures common in CRC (Supplementary Table S6). As illustrated in Fig. 2e, this baseline prediction model correctly identified many of the most frequent hotspots. Mismatches between predicted and observed hotspot frequencies are likely due to selection pressure at the protein level, and/or reflecting the influence of additional local factors such as nucleosome positioning or protein binding sites, all of which have been reported to influence background mutability¹⁶.

To discriminate functional selection for the effect of specific mutations on protein function from the mesoscale features of the nucleotide sequence, we therefore also compared the predicted versus observed frequency of synonymous mutations, as these are not expected to affect protein function (Supplementary Fig. S2, Supplementary Table S7) while sharing the same coarse features (such as replication timing and chromatin state) with non-synonymous mutations. This analysis identified statistically significant differences in patterns of occurrence from predicted frequency for synonymous mutations (p-value 0.0015, Supplementary Fig. S2), suggesting the presence of additional DNA-based mutational processes driving such deviations. However, the deviation from the predicted pattern of non-synonymous mutations (Supplementary Table S7) could not be explained by the same non-selective processes identified in the analysis of the synonymous mutations, as only ~1% of models generated distributions that have metrics similar to that of the data, implying additional selective pressures at the protein level, and/or localized effects involving specific potentially mutable codons.

Loss of LPA activity and protein abundance contribute to selection for PTEN mutational hotspots

Recently published estimates of the damaging potential of almost all possible PTEN mutations^17,18,19, allowed us to characterize most of the observed single amino acid substitutions in PTEN in terms of protein abundance and/or lipid phosphatase activity (LPA) (Supplementary Fig S3a, Supplementary Table S8). Using these data, as well as computational tools to predict the impact of in frame deletions on PTEN protein function²⁰ and annotations for specific PTEN mutations in databases linking genomic variation with phenotypes, we were able to estimate the damaging potential for >90% of non-synonymous mutations in PTEN (non-hypermutated subset), dichotomizing them as functional wildtype, versus LoF (Fig. 3a, Supplementary Fig S3b, Supplementary Table S8). This analysis indicated that hotspot mutations were significantly more likely to be annotated as LoF than non-hotspot mutations, in both the MT-L and MT-H subsets (p-value < 2.2e−16). In general, mutations with greater loss of LPA or abundance are observed at relative frequencies much higher than are those with less effect on protein function (Fig. 3b, Supplementary Fig. S3c), again implying the presence of positive selection for function-damaging PTEN mutations.

**Fig. 3: Functionality of mutationally altered PTEN at least partially defines the observed mutation spectrum.**

We therefore explored if adjusting for the functional status of PTEN mutations (applied as a coefficient calculated as fraction of function-damaging mutations in any given codon) would improve the ability to predict the hotspot profile. Correction for the LPA activity alone provided the best fit with the observed data, leading to a modest improvement in comparison with measures of protein abundance or overall LoF status; and resulted in the overall good congruency between predicted and observed data for single nucleotide substitution data (Fig. 3c, Supplementary Table S9). At least some of the remaining differences are likely due to fine scale mutational features, such as overlaps with mutation consensus sites, or sites with high DNA curvature (Fig. 3d insets, Supplementary Fig. S3d). Thus, as most critical selection processes, the profile of PTEN hotspots is first defined by mutational processes in CRC as defined by mutational signatures, and then further refined by secondary sequence features and positive selection for the loss of LPA.

Evolutionary conservation and allele frequency of PTEN hotspot mutations suggests importance of non-canonical PTEN functions

Mutational hotspots overall are more frequent in evolutionarily conserved positions in protein-coding sequences, based on analysis of the TCGA Pancancer dataset²¹. We also analyzed the location of defined mutational hotspots in the context of the overall evolutionary conservation of both nucleotides and amino acid residues in PTEN (Fig. 4a–c). Across all CRC tumors, hotspot mutations were more likely than non-hotspots to occur in highly conserved amino acids of PTEN (mostly located in the phosphatase domain, Fig. 1c, Supplementary Fig. S3a). However, at the DNA level, hotspots did not occur in more highly conserved nucleotide positions (Supplementary Fig. S3e). These analyses suggested positive selective pressure for PTEN hotspot mutations at the level of protein function.

**Fig. 4: Evolutionary conservation, but not allele frequencies, differ between hotspot and non-hotspot mutations of PTEN.**

Driver mutations that alter protein activity typically occur earlier in tumor evolution, and thus have higher allele fraction (AF), in contrast to passenger mutations, which are expected to appear throughout cancer evolution and are frequently subclonal. For context, we compared the AF of PTEN mutations estimated to have LoF versus WT characteristics in tumors bearing APC, KRAS, PIK3CA, or TP53 mutations (Fig. 4d) in the MT-L subset, comprising the bulk of CRC samples. Considering the complete set of PTEN mutations, the AF of PTEN mutations is lower than that of APC or TP53 mutations, in agreement with the current view that alterations in PTEN are a later event in CRC carcinogenesis²²; however, AFs are similar to those of KRAS, and higher than those of PIK3CA, suggesting selection. However, in the MT-L subset there was no significant difference in AF based on whether or not mutations impaired PTEN function (Fig. 4e). Further, comparison of the individual most abundant hotspots versus the set of all other hotspots, or mutations defined as LoF versus wt, or synonymous (Fig. 4f, Supplementary Table S10) in each case demonstrated a similar allele frequency in MT-L tumors. Comparison of AF of hotspots versus non-hotspots (Supplementary Fig. S3f, g) also demonstrated no significant differences. In contrast to earlier analysis (Fig. 3a–d), these data argued that any positive selection for function-damaging PTEN hotspot mutations is not reflected in their earlier appearance in the tumor evolutionary history.

Finally, we considered that mutations classified as wt-like based on LPA or protein stability may reflect disruption of alternative functions of PTEN, such as protein phosphatase activity, or interaction with substrate. Detailed analysis of the spatial distribution of these “wt” mutations of PTEN did not reveal any propensity to congregate in a particular PTEN domain, or in specific proximity to the sites of post-translational modifications (Supplementary Fig. S3h). Protein interactions have been reported for multiple domains of PTEN (Supplementary Fig. S3h); however, there is currently no available detail about whether the specific wt hotspot mutations would be critical mediators of such interactions.

Distinct co-segregation of PTEN mutations in MT-L and MT-H tumors

In complementary analysis, we asked if specific classes of PTEN alteration selectively co-occurred with common driver mutations that themselves were associated with significant survival effects (Fig. 5a, b). APC, KRAS, TP53, and BRAF mutations have markedly different distribution patterns in MT-L and MT-H cohorts, reflecting the distinct underlying genetic causes of these tumors, and distribution of these mutations has been used to develop a 10-group classification system associated with distinct clinical outcomes¹⁰. The frequency of PTEN alterations varied greatly between these groups in MT-L, but not MT-H tumors. In MT-L tumors, most PTEN alterations are observed in the highly prevalent groups of tumors characterized by mutations in APC + KRAS, APC + TP53, or APC + KRAS + TP53. However, the highest frequency of PTEN alterations is associated with tumors bearing BRAF mutations (BRAF & BRAF + TP53 groups), while the lowest frequency is observed in tumors bearing APC and TP53 alterations in the absence of a KRAS or BRAF driver mutation. These patterns suggest biological selection for PTEN mutations in the context of specific patterns of driver mutations in the MT-L tumors. Intriguingly, more granular analysis of the relationship between PTEN and other driver mutations in MT-L tumors (Fig. 5c) indicates that the mutual exclusion between PTEN and TP53 is driven entirely by the interaction of TP53 mutations and PTEN LoF mutations; in contrast, TP53 deletions tend to co-occur with PTEN deletions and other alterations. In contrast, with the exception of under-representation in tumors with KRAS + TP53 alterations, similar frequencies of PTEN mutations are observed regardless of the profile of driver mutations in MT-H tumors.

**Fig. 5: Correlation of *PTEN* alterations with alterations in other CRC driver genes, and with survival.**

PTEN is a negative regulator of PI3K, which is composed of a catalytic subunit, including PIK3CA, and negative regulatory subunits, including PIK3R1. In CRC, mutations activating PIK3CA or inactivating PIK3R1 occur at a low but appreciable frequency; these mutations significantly co-occurred with both LoF and WT mutations in PTEN (Fig. 5c). Similarly, BRAF and KRAS mutations significantly co-occurred with all classes of PTEN mutations. Additional analysis of the FMI and PAD subsets (Supplementary Fig. S4a, b), as well as analysis of the FMI subset for co-occurrence of mutations in key CRC driver genes, with either individual PTEN mutational hotspots, or cumulatively for belonging to other hotspots, or non-hotspots, also revealed no significant differences in co-occurrence frequency in MT-L (Supplementary Fig. S4c, d) or MT-H (Supplementary Fig. S4e) tumors. Considering co-occurrence separately for the different classes of LOF (e.g., by abundance or loss of LPA), in some cases there are differences (e.g., BRAF). This may reflect the importance of protein-protein interactions in which PTEN plays a non-catalytic function, which may be retained for PTEN mutant forms with reduced LPA, but reduced along with protein abundance.

PTEN mutation versus deletion; relation to prognosis

Although most analyses of PTEN focus on mutation or epigenetic loss of expression of the gene, in some tumors, PTEN is only lost by deletion. Deletions of the PTEN gene were observed in the CRC data set, mostly in MT-L tumors, making up about 40% of all alterations (1228/3311 specimens with PTEN alterations in the combined FMI + PAD dataset). PTEN deletions most commonly co-occurred with the loss of the neighboring genes (Fig. 5d), as well as with the mutation of BRAF, KRAS, and/or PIK3R, and deletions in TP53, and were mutually exclusive with TP53 mutations and PIK3CA mutations (Supplementary Table S11).

We evaluated the impact of PTEN mutation versus loss on overall survival of CRC patients (Fig. 5e). An initial analysis of pooled CRC specimens suggested opposing effects of non-synonymous PTEN mutations and PTEN deletions, with PTEN deletions associated with shorter survival (median overall survival (OS) 31.3 versus 63.2 months), but PTEN mutations with better survival (median OS not reached but >150 months). Independent analysis of the MT-L (Fig. 5f, g) and MT-H (Fig. 5h) cohorts confirmed that PTEN deletions were associated with worse outcome in MT-L patients (median OS 31.3 versus 60.0 months) (Fig. 5f). In contrast, non-synonymous mutations had no effect on survival in MT-L tumors (Fig. 5g), even though ~90% of them caused loss of PTEN LPA function. Intriguingly, a positive survival benefit of PTEN mutations was confined to MT-H tumors; however, this result did not reach statistical significance, likely due to inadequate sample size (Fig. 5h). This was unlikely to reflect specific cosegregation of PTEN mutations with specific driver mutations, which was not observed in MT-H tumors (Fig. 5b). TNM status could potentially influence outcome, if PTEN mutations were selectively associated with late-stage tumors. While stage information is not available for FMI specimens, we analyzed PAD data for 3221 MT-L samples and 796 MT-H samples (Fig. 5i, Supplementary Table S12). This indicated frequency of PTEN mutations was comparable in early (stage I and II) versus late (stage III and IV) tumors, excluding this interpretation.

In contrast, specific association of PTEN deletions with poor outcome in MT-L tumors may partially reflect the effect of alterations in other driver genes co-segregating with these deletions (Fig. 5a; e.g., mutation of either BRAF, KRAS, or PIK3R1, or deletion of TP53 results in shorter survival (48 vs 68 months)). However, it may also reflect the loss of genes linked to PTEN in addition to or instead of PTEN itself. Analysis of the breakpoints of PTEN-encompassing deletions in CRC and other tumor types (Fig. 5d) indicated that genes commonly co-deleted with PTEN include KLLN, ATAD1, PAPSS2, RNLS, and LIPJ. Separate analysis of deletion patterns of these genes shows they are never in the absence of a co-occurring deletion of PTEN, and that mutations inactivating these genes in CRC are extremely uncommon, affecting <1% of tumors. However, it is possible that loss of one or more of these genes in combination with PTEN loss may worsen disease presentation; for example, loss of expression of PAPSS2 has been linked to colitis and colon cancer²³, and downregulation of KLLN exacerbates the presentation of Cowden syndrome²⁴.

Discussion

The predictive and prognostic value and functional role of PTEN alterations in various tumor types has long been an issue of clinical interest²⁵. Although the relatively low frequency of genomic PTEN alterations in CRC has limited analysis of selection pressures in this tumor type, the large cohort analyzed in this study allows us to separate distinct contributions of mutational signature frequency and effect on protein function on the relative abundance of PTEN changes observed in MT-L and MT-H tumors, to establish co-segregation patterns with driver mutations, and to correlate PTEN alterations with overall survival. There are several key conclusions from these data. First, the prevalence of specific mutational signatures and other features of DNA strongly influence, but incompletely explain, the pattern of non-synonymous hotspot mutations observed in PTEN. Second, there is a strong selection for mutations that affect protein stability or lipid phosphatase activity of PTEN, independent of DNA signatures; related to this point, mutations are more likely to target highly conserved amino acids. Third, analysis of the allele frequency of PTEN mutations in MT-L tumors indicates that they occur as early in tumor evolution as do KRAS driver mutations, and earlier than PI3KCA mutations, suggesting that losing PTEN tumor suppressor activity may result in tumor development, at least in some cases. Opposing this idea, the fact that no significant difference is observed between AF of PTEN LoF mutations, versus those functionally wild type, arguing against functional selection based on lipid phosphatase activity or stability. Such functions might include control of PTEN intracellular localization²⁶, protein phosphatase activity²⁷, non-catalytic scaffolding activity²⁸, or interaction with regulatory proteins²⁹.

Fourth, PTEN mutations selectively occur within CRC subsets defined by the presence of specific driver mutations in MT-L tumors, including tumors driven by BRAF, BRAF + TP53, APC + KRAS, or APC mutations; in contrast, PTEN mutations are least likely to occur in tumors bearing APC + TP53, TP53, KRAS + TP53, KRAS, or APC + KRAS + TP53 mutations (P < 2.2e−16), or lacking mutations in any of these genes. However, in many cases the frequency of cosegregation is similar whether the alteration in PTEN is predicted to cause LoF or not. In contrast, no specific pattern of co-occurrence is observed in MT-H tumors. Fifth, PTEN alterations appear to have prognostic value in an unselected set of CRCs, with deletions associated with worse outcome but mutations associated with an improved outcome. However, more detailed analysis of MT-L tumors indicates that mutations of PTEN have no prognostic value, whereas deletions, comprising about 40% of all PTEN alterations, are associated with worse outcomes that may reflect the biological function of loss of the PTEN protein or alternatively, may reflect the tendency of these deletions to co-segregate with driver mutations that are themselves associated with worse prognosis. Finally, in an intriguing finding, analysis of MT-H tumors suggests that in this cohort, PTEN mutations may be associated with greatly improved survival.

Interpretation of these findings is complicated, particularly as such interpretation informs the potential use of PTEN mutations for clinical prognosis. However, overall, several observations argue in favor of the idea that loss of PTEN activity promotes tumor growth in some but not all contexts of driver mutations. Most convincing among these is the fact that the majority of PTEN hotspot mutations (including 3D hotspots) are associated with loss of LPA function, in both MT-L and MT-H tumors. Also suggestive is the specific pattern of segregation observed in MT-L tumors, which reveals a strong bias towards selection of PTEN alterations in tumors bearing BRAF mutations. KRAS is the most common driver oncogene in CRC and many other tumors, based in large part on its ability to simultaneously activate BRAF, PI3K, and other signaling effectors, which collaborate to induce tumor proliferation, survival, and other processes associated with progression³⁰. The fact that loss of PTEN, which would activate the PI3K effector pathway, specifically co-occurs with BRAF rather than KRAS mutation is compatible with the strong selection for activation of both RAS effector pathways. Notably, in all cases, the error bars for correlation with PTEN alterations deemed wild type in phenotype are considerably larger than those for LoF PTEN mutations. While this may partially reflect the lower abundance of these mutations, it may also reflect more uncertainty in calling the functional consequences of these mutations. For instance, these mutations may lead to the altered interactions between PTEN and protein partners; notably, there are >840 potential PTEN interactors listed in the BioGrid database (https://thebiogrid.org/111700/summary/homo-sapiens/pten.html), of which ~50 have so far been validated and are high confidence³¹.

The observed mutual exclusion of TP53 and PTEN mutations is also interesting. TP53 is a transcriptional activator of PTEN³²; conversely, PTEN blocks activation of MDM2, a ubiquitinating enzyme that inhibits TP53, and PTEN directly binds TP53 and regulates its transcriptional activity^33,34. Mutual exclusivity of PTEN and TP53 mutations has previously been documented in some breast cancers³⁵, although the underlying mechanism has not been explored, and in some cancer types, dual loss of PTEN and TP53 contributes to robust tumorigenesis³⁶. However, the fact that function-altering mutations rather than deletions in TP53 and PTEN are mutually exclusive clearly implies altered activity of the proteins may contribute to the exclusion phenotype in CRC. Further analysis investigating PTEN functional interactions with TP53 and other driver mutations, and incorporating transcriptional data, would be valuable as data becomes available. The lack of transcriptional data hinders comparison of the results of this study with other classification systems, such as the Consensus Molecular Subtypes (CMS) from the Colorectal Cancer Subtyping Consortium³⁷, which depend heavily on use of transcriptomic profiling; conversely, only 2 of 18 datasets used to derive CMS have data on PTEN mutations. However, CMS1, comprising most of the MSI/MT-H samples, shows a tendency for higher PTEN mutations frequency (as well as for BRAF and KRAS mutations), in good concordance with our data. CMS3 appears to have a significantly lower frequency of PTEN deletions³⁷. Direct study of these trends in an extended dataset is required.

A limitation of the current study is the lack of information for PTEN mRNA or protein levels in the FMI dataset, particularly as loss of PTEN expression in tumors is known to be induced by various forms of epigenetic regulation, including abundance of specific transcription factors that induce its expression, promoter hypermethylation, regulatory microRNAs, PTEN mRNA binding proteins, expression of the PTENP1 pseudogene^{38,39,40,41,42}. PTEN activity is further modified by a number of post-translational modifications that limit or enhance its activity⁴³. To date, few studies have addressed the degree to which these mechanisms are pertinent in CRC; it is plausible that the contribution of reducing PTEN activity to CRC carcinogenesis is significantly underestimated at present.

Overall, the data in this study suggest that PTEN mutational status can have prognostic value in some CRC tumors, particularly in the set of MT-L tumors with PTEN deletions, and strikingly, for MT-H tumors with PTEN mutations. However, the basis for this prognostic value at present remains unclear, and for the MT-L tumors may well reflect the correlated deletion of PTEN with specific driver mutations that themselves are associated with poor prognosis. One question emerging from this analysis is whether the difference in prognosis associated with deletion rather than mutation of PTEN on prognosis reflects a property intrinsic to PTEN, or rather involves the loss of genes adjacent to PTEN on the chromosome, as deletions of PTEN typically involve simultaneous loss of KLLN, ATAD1, and PAPSS2. KLLN, encoding killin, is frequently lost together with PTEN in Cowden’s syndrome⁴⁴, and contributes to DNA damage-induced apoptosis⁴⁴ and control of genome stability⁴⁵. ATAD1 (ATPase family AAA domain-containing 1) is a member of a small family of proteins that influence mitochondrial function and cellular stress response, some of which have been linked to a role in cancer progression⁴⁶; The loss of ATAD1 predisposes cancer cells to apoptosis triggered by proteasome dysfunction, and alters survival in some cancer types⁴⁷. PAPSS2 encodes an enzyme that mediates the sulfation of numerous cellular proteins, including the mucins that form a protective layer of the intestinal epithelium; loss of PAPSS2 has been shown to promote colitis and colon cancer²³. The contribution of loss or epigenetic silencing of these PTEN-associated genes to the presentation of colorectal cancer has not to our knowledge been addressed.

A confounding factor to assessing the functional role of PTEN mutations in prognosis is the changing landscape of treatments for CRC over the past two decades, with initial dependence on cytotoxic chemotherapies augmented with use of antibodies targeting EGFR (typically for tumors lacking strongly activating KRAS mutations) regorafenib, and tiparacil/trifluidine. More recent investigations have focused on immunotherapies (for MSI-H tumors), and clinical trials are assessing additional protein-targeted inhibitors, including PI3K inhibitors. At present, although treatment information for large cohorts receiving a single treatment regimen is limited, there have been some studies investigating the impact of PTEN mutations or loss on response to these therapies. Given the importance of PI3K as a signaling effector for EGFR, a number of studies have implicated loss of PTEN in resistance to the EGFR-targeting antibodies cetuximab and panitumumab⁴⁸. Unsurprisingly, both PTEN loss and activation of PI3K have been linked to resistance to PI3K-targeting drugs^49,50. The data in this study emphasize some of the complexity of interpreting PTEN data in the context of resistance: for example, PTEN mutations might exert a dominant-negative effect over the wild-type protein, leading to AKT pathway activation; catalytically inactive PTEN might retain lipid phosphatase-independent activities such as promoting DNA repair that are lost after PTEN deletion. Such differences may have substantial effects on prognosis for survival after treatment.

Some work has linked PTEN loss to an immunosuppressive tumor microenvironment in various tumors^51,52,53, which is likely to be an important consideration and topic for future mechanistic studies, given the rise in this class of agent. Although revolutionary in clinical cancer care, significant numbers of patients do not respond to immunotherapy. Understanding why these tumors are refractory to immunotherapy will help to improve the efficacy of treatment. Further, analyzing genes and proteins in both immune-sensitive and immune-resistant settings for differences in structure and function may provide further clues about how to predict and elicit responses. The data in this study emphasizes that it will be important to consider the potential predictive and prognostic value of PTEN loss with some degree of nuance, considering class of mutation, tumor subclass, and co-mutation spectrum.

Methods

Datasets analyzed

Comprehensive genomic profiling (CGP) was performed using the FoundationOne^® or FoundationOne®CDx assays (Foundation Medicine, Inc, Cambridge, MA, USA), as previously described⁵⁴, using specimens collected from 2015–2019. Comparison data sets for studies with information on PTEN mutation status, and sex, age, and tumor subsite were collected from the cBioPortal for Cancer Genomics, https://www.cbioportal.org; AACR Project GENIE, https://genie.cbioportal.org⁵⁵; the Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic⁵⁶; ICGC, https://dcc.icgc.org⁵⁷; and from data included in the supplementary tables of^10,58. To remove redundancy of the collected data, we implemented the following prioritization rules: 1) All GENIE v.13 samples which overlapped with the studies in cBioPortal were removed. 2) For samples with outcomes progressively described in multiple publications, the studies reported in cBioPortal as of 01.01.2023 were used to establish survival data. 3) For samples without microsatellite (MS) status, but with measures of MS instability predicted by MSI prediction algorithms (e.g., SENSOR⁵⁹ or MANTIS⁶⁰ scores), we assigned MS status according to the cutoffs used in the corresponding studies (3.5 and 0.4, respectively; see below). 4) In cases where multiple specimens were collected from a single patient, results from the primary tumor were taken rather than those from metastases. The resulting PAD dataset is presented in Supplementary Table S1.

MSI status assignment

Samples with available TMB data but lacking MS data were classified as previously described at length in ref. ⁵. Briefly, based on extensive statistical analysis of TMB distributions, tumors with TMB between 16 and 100 were classified as likely MSI, grouped with known MSI tumors, and designated MT-H; those with TMB < 16 were grouped with known MSS tumors, and designated MT-L. The small number of samples with TMB > 100 were classified as hypermutated MSS tumors (i.e. likely bearing POLD or POLE mutation), annotated as having a high tumor mutation burden (MSS-htmb). These were removed from subsequent analyses of the MSS/MT-L sub-group, based on known differences in tumor mutational pressures.

To assign MSI status for specimens without pre-calculated TMB (i.e. samples subjected to targeted sequencing using proprietary panel testing for a limited number of genes, e.g. oncopanels), we first determined which of the available parameters (CNA fraction, presence of the mutations in MMR genes, and mutation count) would best partition TCGA COADREAD data into MSS and MSI-H subsets. For this step, we used the R package “party”⁶¹ with minimum criteria of 0.999 (p-value 0.001). Parameters identified in this step were further used to predict the MSI status for each oncopanel. To assess the classification performance of our approach, we used two validation sets for tumors with known MSS and MSI-H status and TMB from ref. ⁶² (Supplementary Table S1, oncopanels IMPACT341 (202 samples) and IMPACT410 (888 samples)), and achieved high classification performance (AUC-ROC of 1 and 0.94, correspondingly).

Statistical analysis

Data were analyzed in R version 4.0.3 using RStudio. Relationships between predicted and observed mutational profiles were assessed using modified chi-square test. Lipid phosphatase activity (LPA), abundance, and allele fraction (AF) profiles were compared using a Kolmogorov–Smirnov (KS) test. Co-occurrence or mutual exclusion of mutations was calculated using a Fisher’s exact test. To account for multiple comparisons of various types, we have lowered the threshold for statistical significance tenfold, to 0.005. Single-residue and 3D hotspots were identified using previously described methods⁸.

To improve identification of 3D hotspots, the ESMfold model was generated using the Web resource (https://esmatlas.com/) and AlphaFold2 models were generated using ColabFold v.1.2.0⁶³. Specifically, the AlphaFold2-ptm model type with 24 recycles was used with the MMSeq2 (Uniref + Environmental) and unpaired + paired mode, based on amino acid residues 1–354 of PTEN. Models were subsequently refined using OpenMM and Amber Force Fields⁶⁴. The local Distance Difference Test (lDDT) was used to assess local model quality, and 5 top ranked models were selected. Two residues with any pair of atoms within 5 Å were considered in contact, with distances calculated using the Residue Interaction Network Generator (RING) (ref. ⁶⁵, https://ring.biocomputingup.it/submit). 3D hotspots were determined as previously described⁵. To determine amino acids at the interface between two PTEN molecules, we identified all pairs of residues in contact between two chains in 5BUG [https://www.rcsb.org/structure/5BUG], 5BZX [https://www.rcsb.org/structure/5BZX]), and 5BZZ [https://www.rcsb.org/structure/5BZZ]). We considered the contact between the two residues as true if proximity was reproducible in at least 4 out of 6 chain-chain interacting pairs. PyMol (ref. ⁶⁶, https://pymol.org/2/) was used for the model alignment, as well as for the visualization of relevant residues on the corresponding structures.

Mutational signatures

Information on the mutational signatures prevalent in colorectal cancer was downloaded from the COSMIC database (version 3.1); https://cancer.sanger.ac.uk/cosmic/signatures). Compatibility of the detected mutation with a given signature was determined by matching observed mutations to the most frequent nucleotide base changes, together with its trinucleotide context, in each signature.

Assessment of PTEN variant functionality

Estimates of lipid phosphatase activity and protein abundance were based on data reported in ref. ¹⁸. Prediction of the impact of in frame deletions on protein function were done using MutPred2²⁰, and annotations in clinical databases were retrieved from ref. ⁵.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The sequencing data provided by Foundation Medicine in this study are derived from clinical samples. All consented data supporting the findings of this study that can be released are provided within the article and its supplementary files. Due to HIPAA requirements, we are not authorized to share underlying sequence data or individualized patient genomic data, which contain potentially identifying or sensitive patient information. Foundation Medicine, Inc. is committed to collaborative data analysis, and it has well-established and widely utilized mechanisms by which investigators who have signed a data transfer agreement can query its core genomic database of >600,000 deidentified sequenced cancers to obtain aggregated datasets. For more information and mechanisms of access to the Foundation Medicine, Inc. data in this study, please contact the corresponding authors or the Foundation Medicine, Inc. Data Governance Council at data.governance.council@foundationmedicine.com. Publicly available web resources used in this paper are listed here: The cBioPortal for Cancer Genomics, https://www.cbioportal.org ; AACR Project GENIE, https://genie.cbioportal.org; the Catalogue Of Somatic Mutations In Cancer, https://cancer.sanger.ac.uk/cosmic; the Surveillance, Epidemiology, and End Results (SEER) Program, https://seer.cancer.gov; ICGC, https://dcc.icgc.org. PyMol files for the visualization of hotspots on the PTEN structure (used in Fig. 1d, e) are available in the corresponding folders at Github (https://github.com/NDeeSeee/PTEN_CRC_2023/paper).

Code availability

The code used for the data analysis in this study is deposited at Zenodo (https://zenodo.org/record/8423389).

References

Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin. 71, 7–33 (2021).
Article PubMed Google Scholar
Meyer, JE, Cohen, SJ, Ruth, KJ, Sigurdson, ER, Hall, MJ. Young age increases risk of lymph node positivity in early-stage rectal cancer. J. Natl Cancer Inst. 108, djv284 (2016).
Meyer, J. E., Narang, T., Schnoll-Sussman, F. H., Pochapin, M. B., Christos, P. J. & Sherr, D. L. Increasing incidence of rectal cancer in patients aged younger than 40 years: an analysis of the surveillance, epidemiology, and end results database. Cancer 116, 4354–4359 (2010).
Article PubMed Google Scholar
Golemis, E. A. et al. Molecular mechanisms of the preventable causes of cancer in the United States. Genes Dev. 32, 868–902 (2018).
Article CAS PubMed PubMed Central Google Scholar
Serebriiskii, I. G. et al. Comprehensive characterization of PTEN mutational profile in a series of 34,129 colorectal cancers. Nat. Commun. 13, 1618 (2022).
Article CAS PubMed PubMed Central Google Scholar
Alvarez-Garcia, V., Tawil, Y., Wise, H. M. & Leslie, N. R. Mechanisms of PTEN loss in cancer: It’s all about diversity. Semin. Cancer Biol. 59, 66–79 (2019).
Article CAS PubMed Google Scholar
Yehia, L., Keel, E. & Eng, C. The clinical spectrum of PTEN mutations. Annu. Rev. Med. 71, 103–116 (2020).
Article CAS PubMed Google Scholar
Serebriiskii, I. G. et al. Comprehensive characterization of RAS mutations in colon and rectal cancers in old and young patients. Nat. Commun. 10, 3722 (2019).
Article PubMed PubMed Central Google Scholar
Lieu, C. H. et al. Comprehensive genomic landscapes in early and later onset colorectal cancer. Clin. Cancer Res. 25, 5852–5858 (2019).
Article CAS PubMed PubMed Central Google Scholar
Schell, M. J. et al. A multigene mutation classification of 468 colorectal cancers reveals a prognostic role for APC. Nat. Commun. 7, 11743 (2016).
Article CAS PubMed PubMed Central Google Scholar
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 173, 1823 (2018).
Article CAS PubMed PubMed Central Google Scholar
Chandrashekar, P. et al. Somatic selection distinguishes oncogenes and tumor suppressor genes. Bioinformatics 36, 1712–1717 (2020).
Article CAS PubMed Google Scholar
Cook, J. H., Melloni, G. E. M., Gulhan, D. C., Park, P. J. & Haigis, K. M. The origins and genetic interactions of KRAS mutations are allele- and tissue-specific. Nat. Commun. 12, 1808 (2021).
Article CAS PubMed PubMed Central Google Scholar
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Article CAS PubMed PubMed Central Google Scholar
Montgomery, S. B. et al. The origin, evolution, and functional impact of short insertion-deletion variants identified in 179 human genomes. Genome Res. 23, 749–761 (2013).
Article CAS PubMed PubMed Central Google Scholar
Buisson, R, et al. Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale genomic features. Science 364, eaaw2872 (2019).
Mighell, T. L., Evans-Dutson, S. & O’Roak, B. J. A saturation mutagenesis approach to understanding PTEN lipid phosphatase activity and genotype-phenotype relationships. Am. J. Hum. Genet. 102, 943–955 (2018).
Article CAS PubMed PubMed Central Google Scholar
Mighell, T. L., Thacker, S., Fombonne, E., Eng, C. & O’Roak, B. J. An integrated deep-mutational-scanning approach provides clinical insights on PTEN genotype-phenotype relationships. Am. J. Hum. Genet. 106, 818–829 (2020).
Article CAS PubMed PubMed Central Google Scholar
Cagiada, M. et al. Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance. Mol. Biol. Evol. 38, 3235–3246 (2021).
Article CAS PubMed PubMed Central Google Scholar
Pejaver, V. et al. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat. Commun. 11, 5918 (2020).
Article CAS PubMed PubMed Central Google Scholar
Chen, T. et al. Hotspot mutations delineating diverse mutational signatures and biological utilities across cancer types. BMC Genom. 17, 394 (2016).
Article Google Scholar
Caravagna, G. et al. Algorithmic methods to infer the evolutionary trajectories in cancer progression. Proc. Natl Acad. Sci. USA 113, E4025–4034 (2016).
Article CAS PubMed PubMed Central Google Scholar
Xu, P. et al. Intestinal sulfation is essential to protect against colitis and colonic carcinogenesis. Gastroenterology 161, 271–286.e211 (2021).
Article CAS PubMed Google Scholar
Bennett, K. L., Mester, J. & Eng, C. Germline epigenetic regulation of KILLIN in Cowden and Cowden-like syndrome. JAMA 304, 2724–2731 (2010).
Article CAS PubMed PubMed Central Google Scholar
Bazzichetto, C, et al. PTEN as a prognostic/predictive biomarker in cancer: an unfulfilled promise? Cancers 11, 435 (2019).
Bassi, C. et al. Nuclear PTEN controls DNA repair and sensitivity to genotoxic stress. Science 341, 395–399 (2013).
Article CAS PubMed PubMed Central Google Scholar
Davidson, L. et al. Suppression of cellular proliferation and invasion by the concerted lipid and protein phosphatase activities of PTEN. Oncogene 29, 687–697 (2010).
Article CAS PubMed Google Scholar
Zhao, D. et al. Synthetic essentiality of chromatin remodelling factor CHD1 in PTEN-deficient cancer. Nature 542, 484–488 (2017).
Article CAS PubMed PubMed Central Google Scholar
Maddika, S. et al. WWP2 is an E3 ubiquitin ligase for PTEN. Nat. Cell Biol. 13, 728–733 (2011).
Article PubMed PubMed Central Google Scholar
Simanshu, D. K., Nissley, D. V. & McCormick, F. RAS proteins and their regulators in human disease. Cell 170, 17–33 (2017).
Article CAS PubMed PubMed Central Google Scholar
Smith, S. L., Pitt, A. R. & Spickett, C. M. Approaches to Investigating the Protein Interactome of PTEN. J. Proteome Res. 20, 60–77 (2021).
Article CAS PubMed Google Scholar
Stambolic, V. et al. Regulation of PTEN transcription by p53. Mol. Cell 8, 317–325 (2001).
Article CAS PubMed Google Scholar
Freeman, D. J. et al. PTEN tumor suppressor regulates p53 protein levels and activity through phosphatase-dependent and -independent mechanisms. Cancer Cell 3, 117–130 (2003).
Article CAS PubMed Google Scholar
Mayo, L. D., Dixon, J. E., Durden, D. L., Tonks, N. K. & Donner, D. B. PTEN protects p53 from Mdm2 and sensitizes cancer cells to chemotherapy. J. Biol. Chem. 277, 5484–5489 (2002).
Article CAS PubMed Google Scholar
Kurose, K. et al. Frequent somatic mutations in PTEN and TP53 are mutually exclusive in the stroma of breast carcinomas. Nat. Genet 32, 355–357 (2002).
Article CAS PubMed Google Scholar
Perets, R. et al. Transformation of the fallopian tube secretory epithelium leads to high-grade serous ovarian cancer in Brca;Tp53;Pten models. Cancer Cell 24, 751–765 (2013).
Article CAS PubMed PubMed Central Google Scholar
Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med 21, 1350–1356 (2015).
Article CAS PubMed PubMed Central Google Scholar
Chang, H, Cai, Z, Roberts, TM. The mechanisms underlying PTEN loss in human tumors suggest potential therapeutic opportunities. Biomolecules 9, 713 (2019).
Goel, A. et al. Frequent inactivation of PTEN by promoter hypermethylation in microsatellite instability-high sporadic colorectal cancers. Cancer Res. 64, 3014–3021 (2004).
Article CAS PubMed Google Scholar
Zhang, L. et al. Microenvironment-induced PTEN loss by exosomal microRNA primes brain metastasis outgrowth. Nature 527, 100–104 (2015).
Article CAS PubMed PubMed Central Google Scholar
Sellars, E, Gabra, M, Salmena, L. The complex landscape of PTEN mRNA regulation. Cold Spring Harb. Perspect. Med. 10, a036236 (2020).
Haddadi, N., Lin, Y., Travis, G., Simpson, A. M., Nassif, N. T. & McGowan, E. M. PTEN/PTENP1: ‘Regulating the regulator of RTK-dependent PI3K/Akt signalling’, new targets for cancer therapy. Mol. Cancer 17, 37 (2018).
Article PubMed PubMed Central Google Scholar
Chen, L., Liu, S. & Tao, Y. Regulating tumor suppressor genes: post-translational modifications. Signal Transduct. Target Ther. 5, 90 (2020).
Article CAS PubMed PubMed Central Google Scholar
Sankunny, M. & Eng, C. KLLN-mediated DNA damage-induced apoptosis is associated with regulation of p53 phosphorylation and acetylation in breast cancer cells. Cell Death Discov. 4, 31 (2018).
Article PubMed Google Scholar
Nizialek, E. A., Sankunny, M., Niazi, F. & Eng, C. Cancer-predisposition gene KLLN maintains pericentric H3K9 trimethylation protecting genomic stability. Nucleic Acids Res 44, 3586–3594 (2016).
Article CAS PubMed Google Scholar
Lang, L, Loveless, R, Teng, Y. Emerging links between control of mitochondrial protein ATAD3A and cancer. Int. J. Mol. Sci. 21, 7917 (2020).
Winter, JM, et al. Collateral deletion of the mitochondrial AAA+ ATPase ATAD1 sensitizes cancer cells to proteasome dysfunction. eLife 11, e82860 (2022).
Salvatore, L, et al. PTEN in colorectal cancer: shedding light on its role as predictor and target. Cancers 11, 1765 (2019).
Juric, D. et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kalpha inhibitor. Nature 518, 240–244 (2015).
Article CAS PubMed Google Scholar
Razavi, P. et al. Alterations in PTEN and ESR1 promote clinical resistance to alpelisib plus aromatase inhibitors. Nat. Cancer 1, 382–393 (2020).
Article CAS PubMed PubMed Central Google Scholar
Chida, K. et al. A low tumor mutational burden and PTEN mutations are predictors of a negative response to PD-1 blockade in MSI-H/dMMR gastrointestinal tumors. Clin. Cancer Res. 27, 3714–3724 (2021).
Article CAS PubMed Google Scholar
Peng, W. et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 6, 202–216 (2016).
Article CAS PubMed Google Scholar
Melo, CM. et al. The role of somatic mutations on the immune response of the tumor microenvironment in prostate cancer. Int. J. Mol. Sci. 22, 9550 (2021).
Frampton, G. M. et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat. Biotechnol. 31, 1023–1031 (2013).
Article CAS PubMed PubMed Central Google Scholar
Consortium, A. P. G. AACR project GENIE: powering precision medicine through an International Consortium. Cancer Discov. 7, 818–831 (2017).
Article Google Scholar
Forbes, S. A. et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777–D783 (2017).
Article CAS PubMed Google Scholar
Zhang, J. et al. The international cancer genome consortium data portal. Nat. Biotechnol. 37, 367–369 (2019).
Article CAS PubMed Google Scholar
Giannakis, M. et al. RNF43 is frequently mutated in colorectal and endometrial cancers. Nat. Genet 46, 1264–1266 (2014).
Article CAS PubMed PubMed Central Google Scholar
Niu, B. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).
Article CAS PubMed Google Scholar
Kautto, E. A. et al. Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS. Oncotarget 8, 7452–7463 (2017).
Article PubMed Google Scholar
Buri, M., Tanadini, L. G., Hothorn, T. & Curt, A. Unbiased recursive partitioning enables robust and reliable outcome prediction in acute spinal cord injury. J. Neurotrauma 39, 266–276 (2022).
Article PubMed Google Scholar
Yaeger, R. et al. Clinical sequencing defines the genomic landscape of metastatic colorectal cancer. Cancer Cell 33, 125–136.e123 (2018).
Article CAS PubMed PubMed Central Google Scholar
Mirdita, M., Schutze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S. & Steinegger, M. ColabFold: making protein folding accessible to all. Nat. methods 19, 679–682 (2022).
Article CAS PubMed PubMed Central Google Scholar
Eastman, P. et al. OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 13, e1005659 (2017).
Article PubMed PubMed Central Google Scholar
Clementel, D. et al. RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles. Nucleic Acids Res. 50, W651–W656 (2022).
Article CAS PubMed PubMed Central Google Scholar
DeLano, W. The PyMOL User’s Manual (DeLano Scientific, San Carlos, CA, USA, 2002).
Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).
Article CAS PubMed PubMed Central Google Scholar
Chowdhury, K. et al. Presence of a consensus DNA motif at nearby DNA sequence of the mutation susceptible CG nucleotides. Gene 639, 85–95 (2018).
Article CAS PubMed Google Scholar
Chuzhanova, N. A., Anassis, E. J., Ball, E. V., Krawczak, M. & Cooper, D. N. Meta-analysis of indels causing human genetic disease: mechanisms of mutagenesis and the role of local DNA sequence complexity. Hum. Mutat. 21, 28–44 (2003).
Article CAS PubMed Google Scholar
Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–350 (2016).
Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

The results reported here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga, and the AACR Project Genie Consortium. The authors were supported by NCI Core Grant CA006927 (to Fox Chase Cancer Center), NIH CA256234 (to I.G.S.), funding from the W.W. Smith Foundation (to E.A.G.), and by a grant from the Colon Cancer Alliance (to J.M.).

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Authors and Affiliations

Program in Cell Signaling and Microenvironment, Fox Chase Cancer Center, Philadelphia, PA, 19111, USA
Ilya G. Serebriiskii, Valerii A. Pavlov, Grigorii V. Andrianov, Samuel Litwin, Joshua E. Meyer & Erica A. Golemis
Kazan Federal University, 420000, Kazan, Russian Federation
Ilya G. Serebriiskii
Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow Region, Russian Federation
Valerii A. Pavlov
Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA, 19111, USA
Samuel Litwin
Greenfield Manufacturing, 9800 Bustleton Ave, Philadelphia, PA, 19115, USA
Stanley Basickes
Foundation Medicine, Inc., 150 Second St., Cambridge, MA, 02141, USA
Justin Y. Newberg & Garrett M. Frampton
Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, PA, 19111, USA
Joshua E. Meyer
Department of Cancer and Cellular Biology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
Erica A. Golemis

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Ilya G. Serebriiskii
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Contributions

J.N. and G.F. collected data. I.G.S., J.N. and V.P. performed data analysis. E.A.G., I.G.S., C.L. and J.E.M. designed the study and wrote the manuscript. All authors significantly contributed to the interpretation of the analyzed data, and critically revising the manuscript. All authors approved the completed version, and share accountability for all aspects of the work.

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Correspondence to Ilya G. Serebriiskii or Erica A. Golemis.

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J.N. and G.F. are employed by FMI, and own stock in Roche. All other authors declare no competing interests.

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Serebriiskii, I.G., Pavlov, V.A., Andrianov, G.V. et al. Source, co-occurrence, and prognostic value of PTEN mutations or loss in colorectal cancer. npj Genom. Med. 8, 40 (2023). https://doi.org/10.1038/s41525-023-00384-7

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Received: 24 August 2023
Accepted: 07 November 2023
Published: 24 November 2023
DOI: https://doi.org/10.1038/s41525-023-00384-7